Reviewerly/exHarmony huggingface dataset:
Silhouette-Based Haman Action Recognition Using Convolutional Neural Network

Nowadays BAR is gaining interest in computer vision area. The BAR system was utilized and applied in various applications related to surveillance. Some of the surveillance applications are airport surveillance, shop surveillance, bank, schools, and many areas. The goal is to recognize human actions automatically. If the raw video is taken as input without any preprocessing then the human action recognition system consumes more time and leads to increased storage space. So in this work silhouette is prefered to extract the features with a time and space consuming manner. and finally CNN is used to classify the actions. The standard Weizmann dataset and KTH dataset is used to evaluate and manipulate the performance of the proposed HAR technique. The proposed method provides 98 percent accuracy for the Weizmann dataset and 93 percent accuracy for the KTH dataset. The HAR technique provides efficient outcome than the recent existing techniques that are used for used for human action recognition(HAR) systems.
Aiderbot: DTMF Based Medical Robot

Traditionally, high technology robots are applied on medical field known for its accuracy and reliability but the cost of those robots are too expensive but a tool nonetheless. In this paper, we introduce DTMF (Dual Tone Multi Frequency) that can be paired with the robot to control it remotely using telephone with ease for the patients. This robot is capable of functioning various operations like cleaning, serving, recommending doctor or nurse assistant when needed. The robot is controlled with a micro controller which is integrated with DTMF decoder and motors to help robot move around using path memorizing algorithm. With the inter-connection of various essential components and with use of a telephone as a remote we can operate the robot from anywhere with all the safety measures such as security. The cost of this robot to deploy in hospitals is very less when compared to other automated robot services.
Aeroencrypt

Our lives rely heavily on passwords, which are frequently the first line of defense. As a result, passwords should be lengthy and challenging so that they cannot be quickly deciphered or stolen. Complex passwords, however, are challenging to remember since they contain special characters in addition to the conventional 8-character length. As a result, either an easy password is used or a complex password is forgotten. Password managers are used to stop this from happening. However, the fact that practically all password managers are cloud-based raises concerns about security and data leakage. Few offline password managers that are truly secure are available. The synchronization capabilities of an online password manager is another important factor in people's decisions. The features of offline password managers' data security as well as their ability to sync with online password managers are what AeroEncrypt intends to target for PCs and mobile devices, AeroEncrypt serves as an offline password manager. It features multiple layers of encryption and supports device syncing. The encrypted string is implemented using both RSA and AES, and the database as a whole is encrypted. The Graphical User Interface for storing and retrieving passwords is a desktop application. A mobile app will serve as the graphical user interface for mobile devices. By syncing the passwords, it is possible to use the desktop and mobile applications independently as well as simultaneously.
Image Forgery Detection Using Machine Learning

Imaging technology has made tremendous progress in recent years, and digital images can now be captured by a variety of handheld devices such as mobile phones, wearables, tabs, and camcorders. These images are shared daily on social networks. Meanwhile, photo-editing software tools are making a comeback from the dawn of social networking, and filtering is just a few clicks away from your smart phone. These latest advances in image editing tools have eroded trust in digital images and cast doubt on their integrity and authenticity. Here, we use a machine learning algorithm (SVM) to effectively detect if an image has been manipulated and block users who try to upload altered images.
A Fast and Robust Regression Model for Keyframes Extraction

In recent days, multimedia content has been developed from multiple resources such as social media video content, video surveillance, etc. Due to rapid increasing the volume of digital data, analysis and retrieval of the video will be time consuming. Due to continuous frame count, retrieving content of the video from large databases is a challenging one. To overcome the above challenge, this paper introduces a regression model to extract the information that is derived from frame sequence that uses two statistical features on the frame. This model can be applied on any kinds of video data to extend several multimedia applications and experiments for scene understanding, video summarization, etc Finally, user generated video where static video and dynamic videos and VIRAT benchmark dataset are evaluated by demonstrating the effectiveness of the proposed work.
Automation and Monitoring System for Mushroom Cultivation using Mobile application and Esp-32

In this growing economy, vegetables and fruits have been playing a major role. One of those which has greater value is oyster mushrooms. The cultivation of these mushrooms and the conversion rate have been pretty low from the manual cultivation, and there has been a lot of loss in the cultivation. Looking after this problem, we have a proposal of making the cultivation in an automated and simple way with the help of an app that displays the parameters maintained.
A Comprehensive Review on Data Partitioning and Sampling Techniques for Processing Big Data

Big data processing and analysis use shared-nothing computer clusters. Cluster computing relies heavily on data segmentation and sampling to boost the speed and scalability of large data computations. In this study, we provide a thorough review of sampling and data division approaches applicable to large data processing and analysis. Next, we'll go over some of the fundamentals of data partitioning, such as the difference between range partitioning, hash partitioning, and random partitioning. Then, we get into the standard data sampling techniques such stratified sampling, reservoir sampling, and simple random sampling and approaches suitable for clusters. Our proposal is to take both data partitioning and sampling into consideration simultaneously while processing large data sets in parallel environment.
Automatic Solar Tracking System

solar energy has become an increasingly important and popular renewable energy source. By using a solar tracking system, we can produce an abundance of energy and improve the efficiency of solar panels. The solar panel's efficiency lies in its perpendicular proportionality with the sun's rays. Although cheaper options are also available, its installation charge is high. A prototype solar panel is discussed in this paper based on the sun's rays as the reason for its design and construction. Arduino is used as the main control circuit. As a result of the programming of this device, the LDR sensor, when it detects sun rays, will provide direction to the Servo Motor in order to move the solar panel. Consequently, the solar panel is positioned so that it can receive the maximum amount of sunlight.
IoT Based Voice Assistant using Raspberry Pi and Natural Language Processing

In this modern century, everyone benefits from smart assistants. where we can converse with intelligent helpers and ask robots questions. Nearly everyone in the globe is drawn to this technology in a variety of ways, including smartphones, laptops, desktops, etc. It is a smart assistant with speech recognition intelligence that accepts user input in the form of text or voice, processes it, and then provides the output in a variety of ways, such as by speaking the user's search results. Google Assistant, Siri, and Alexa are a few examples of intelligent assistants. They are unable to perform voice recognition, and there are still problems with human interaction. Those Google Assistant problems, in order to communicate with users, they must have access to WiFi and the internet also they are using very limited language so users can get many problems in using them. Google Assistant is a feature of Android smartphones. However, Internet connections are required for this application to function. However, our proposed system can also function without an Internet connection. So that to retifice the problem the to implement multiple languages to that so that we are using raspberry pi for that in the raspberry pi the data of multiple languages is loaded and stored so that the user can access any language through the voice assistant so that the user gets information about recent apps, daily news, geolocation, and Wikipedia. Through voice assistance, the user also gets benefits in using automation technology.
Doctor's Handwritten Prescription Recognition System In Multi-Language Using Deep Learning

Doctors typically write in incomprehensible handwriting, making it difficult for both the general public and some pharmacists to understand the medications they have prescribed. It is not ideal for them to write the prescription quietly and methodically because they will be dealing with dozens of patients every day and will be swamped with work. As a result, their handwriting is illegible. This may result in reports or prescriptions consisting of short forms and cursive writing that a typical person or pharmacist won't be able to read properly, which will cause prescribed medications to be misspelled. However, some individuals are accustomed to writing prescriptions in regional languages because we all live in an area with a diversity of regional languages. It makes analyzing the content much more challenging. So, in this project, we'll use a recognition system to build a tool that can translate the handwriting of physicians in any language. This system will be made into an application which is fully autonomous in functioning. As the user uploads the prescription image the program will pre-process the image by performing image pre-processing, and word segmentations initially before processing the image for training. And it will be done for every language we require the model to detect. And as of the deduction model will be made using deep learning techniques including CNN, RNN, and LSTM, which are utilized to train the model. To match words from various languages that will be written in the system, Unicode will be used. Furthermore, fuzzy search and market basket analysis are employed to offer an end result that will be optimized from the pharmaceutical database and displayed to the user as a structured output.
Emotion Recognition from Facial Expressions Using Deep Learning-CNN Model

Emojis are a type of emoticon that are often used in text messages sent across social media platforms. A contemporary manner of communication is characterized by the inclusion of both textual and graphical information inside the same message. Emoticons and avatars are both examples of non-verbal communication tools. These indicators have rapidly become an important component of a wide variety of activities, including online talking, product reviews, brand emotions, and many others. It also resulted in an increase in the amount of data science research devoted to narratives driven by emojis. It is now feasible, as a result of improvements in computer vision and deep learning, to identify human emotions based only on visual cues. In this deep learning project, we will classify human facial expressions in order to map and filter avatars or emojis that correspond. This project's goal is to make the talking world appear more vibrant; it is not intended to offer a solution to an issue that occurs in the real world. Emojis is a piece of software that makes it easier to create avatars and emojis.
Enhanced Object Detection with Deep Convolutional Neural Networks for Vehicle Detection

Recent years have seen a significant increase in scholarly interest in object detection because of its strong connection to video analysis and picture interpretation. Shallow trainable structures and handcrafted features facilitate conventional object recognition techniques. Intricate ensembles that integrate poor visual elements with high-level data from object detectors and scene classifiers reach an effectiveness threshold relatively quickly. In order to help with problems with conventional architectures, more powerful tools that can learn deeper, higher-level, and more semantic characteristics are becoming available as deep learning develops quickly. For instance, these models behave differently when it comes to network architecture, training strategy, and optimization methodology. In this paper, we evaluate studies on object recognition using deep learning. The authors of the study begin with a primer on deep learning and its principal methods, the convolutional neural network (CNN). The subject will then shift to a number of widely used generic object detection methods, as well as various improvements and practical strategies for improving detection generally. The topic of different common genealogical patterns for object recognition will next be covered, along with some tweaks and practical methods for enhancing detection. even more performance It also briefly explores a number of particular detection tasks, such as pedestrian identification, face detection, and recognition of remarkable items because they display a range of properties. Experimental analyses are also available to compare alternate strategies and get some useful results. The recommendations for further research in both the object and object-oriented fields cover a wide range of prospective directions and objectives.
Hand Gesture Controlled Wheelchair

This paper outlines a method for directing wheelchair movement through hand gestures. The wheelchair is made in such a way that any individual may control it with ease if they just move their wrist. The hand movements are recognized by using hand gesture recognition algorithm. It recognizes five different hand movements like forward, reverse, left, right and stop. It does not involve complex computing system which is quite challenging for the elderly people. This design also has GPS features to track location of the wheelchair from anywhere in the world. It is also connected by using Bluetooth technology which helps the people to control through assisting people.
Speech Processing and Recognition of Sign Language using Tensor flow Classifier

Communication facilitates social interaction and perspective exchange. Therefore, the deaf and mute face significant obstacles when attempting to participate in society. People are only able to communicate through the use of sign language. It is possible to translate sign languages into a form that can be used for public communication. To this end, we will develop a real-time system that can interpret and translate Sign Language into written language. The majority of the operation relies on custom-made components such as gloves and robotic arms. To complete this task, we employ a convolutional neural network and a deep learning strategy. Beginning with the Python library Keras for training convolutional neural networks, a sign-based classifier model is developed. Then, we utilised the real-time skin segmentation system to locate the Region of Interest within the bounding box. The classified region is utilised to predict the symbol. The classifier was found to be more effective against complex backgrounds and camera angles, and to translate sign language into speech and text that the hearing-impaired can comprehend.
An Efficient Task scheduling and Data security in Heterogeneous Cloud Computing using Hybrid Meta-Heuristic Algorithm and Block Chain based Key Aggregate Cryptosystem

Cloud computing is a rapidly growing technology which is hugely used in various fields and it offers a strong and fashionable corporate environment. However, cloud computing environment faces many challenges in scheduling and securing the huge amount of data provided by various users. To overcome the conventional performance limitations in resource scheduling and data security various optimization and encryption models are evolved in recent times. However, the performances can be improved if hybrid models are employed. Thus, a hybrid Ant Colony Optimization-Variable Neighborhood Search(ACO-VNS) metaheuristic algorithm is presented in this research work to improved task scheduling. Furthermore, to handle user data security and privacy a combination of Attribute Based Encryption (ABE) with (BKAC) blockchain based key aggregate cryptosystem technique is presented. simulation analysis includes standard benchmark datasets and the performances are compared to existing techniques such as Energy -Efficient Dynamic Scheduling Scheme (EDS), Cross Entropy based Virtual Machine (CEVP), Meta-Heuristic Crow Search Algorithm (H3CSA), PPSO (Parallel Particle Swarm Optimization) and Block Chain based CP-ABE Scheme with Partially Hidden Access Policy (BCP-ABE-PHAS) to validate the superior performance. The experimental result demonstrates the performance of the proposed meta-heuristic and ABE-BKAC model in terms of completion ratio, response time, make span, energy consumption, keygen time, Encryption Time and Decryption Time.
Enhancement of Solar Output with Dual Axis Solar Tracker using Arduino

Collection of Sun's energy at the maximum level can be simply possible by using the tracking system. The proposed network aims in tracking the direction of highest radiation angle of sun's beam. This is done by fixing the panel with the geared motors embedded with sensors and controllers. Two servo motors are controlled by the c program embedded in the arduino controller and the LDR sensor. This method gives high output when working with tracker than without tracker and gives high efficiency. This can be applicable in simple low voltage systems where the complex techniques and algorithms cost more. The output power of the system with tracker and without tracker was studied. The voltage and current characteristics of the system with tracker and without tracker for different time period from morning to evening was found and drawn.
Speech Recognized Face Composition Tool Using IoT

A face synthesis is a graphic representation of one or more eyewitness memories of a face recorded by a synthesis artist. Creating highly accurate forensic sketches typically requires several years of training before a forensic sketcher is proficient at drawing sketches. To address this issue, we develop a software tool that uses audio and image processing techniques to create facial composites based on eyewitness dictation narration. Successful technology that automatically matches face sketches to mugshots could also improve the effectiveness of face sketches, allowing such suspects to be apprehended more quickly.
A Framework of Medical Image Segmentation using Edge Stop Function based on Contour Model

Liver cancer is a major taker of lives in recent years. Generally, a liver cancer death rate is very high because the disease shows no symptoms and it's often not caught until the final stages. Traditional Segmentation methods include Region-Based which is based on Intensity and Gradient information which can be applied only when the images has constant gradient information. The Edge-Based Segmentation technique, though can be applied to images with diverse homogeneities, can't be applied to images with non-definitive boundaries. We propose a novel method for liver cancer detection using robust edge stop functions based on Contour detection and Artificial Neural Networks with the Wavelet Features separated from the source input image. By incorporating the gradient information along with the probability scores from a typical classifier, an algorithm is constructed which is applied for the ESF Model. Also the wavelet features classify a dataset and then get the result in normal and abnormal stages. The K-nearest neighbors and SVM approve the effectiveness of the proposed approach.
FSM Based DNA Assembly Using Systolic Array

In this paper, we investigate the feasibility of computing DNA patterns on FPGAs by utilising high-performance and parallel computing. In particular, the report analyses and contrasts the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the dynamic and heuristic alternatives that are currently on the market. In this research project, we investigate the use of parallel and high-performance computing to determine how DNA patterns can be computed on FPGAs. In particular, the report details the findings of a study that compared the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the algorithm's current dynamic and heuristic programming methods. The study was carried out by comparing these three aspects of the algorithm. The traditional methods of sequence alignment that are software-implied are not adequate to meet the actual data rate requirements. A strategy that is based on the use of hardware makes it possible to process multiple new databases in parallel, which enables a high degree of scalability. A finite state machine serves as the primary data processor in our demonstration of how to organize the sequences of amino acids in a protein (FSM). We also investigate the effectiveness of bit-based sequence alignment algorithms and present the inner stage pipelined FPGA architecture for implementing sequence alignment. Both of these are presented in this paper. Synchronized controllers are utilized in the programming techniques of the algorithm for the purpose of parallel sequence alignment. Inference is the foundation of traditional software-based methods for sequence alignment, but these methods are unable to keep up with the required data rates. A strategy that is based on hardware allows for the processing of parallel tasks with a large number of new databases and provides excellent scalability. Our explanation of the classification of protein sequences is based on a Finite State Machine, which serves as the primary processing unit (FSM). In addition to this, we investigate how well bit-based sequence alignment algorithms work and present the inner stage pipelined Field Programmable Gate Array architecture that can be used to implement sequence alignment. Synchronized controllers are utilized in the process of performing sequence alignment in parallel. The architecture as a whole was made to be scalable and fast, with a small number of computations and the ability to perform parallel processing in hardware using field-programmable gate arrays to compare bits in patterns. Finally, we use FPGA hardware synthesis to show off impressive performance and low resource consumption.