Reviewerly/exHarmony huggingface dataset:
Emotion Detection in Online Social Network- A Multilabel Learning Process

Online social networks (OSNs)that provide emotion detection might benefit from a variety of programmers, such as specialized commercial services, guidance systems, etc. Many published materials in OSNs offer a great chance to investigate users' emotions, making it easier to design programmers quickly that are responsive to theirusers' emotions. According to the people's present emotions, personalized recommendation systemcan make recommendations for specific goods, movies, or songs. With an annotated Twitter dataset, this research creates connections between emotion labels, social relationships, and chronological events. In this research work we use an emotion factor graph identification model to integrate social relationships, sequential correlations and sentiment label connections are combined into a common framework, and we identify a few completely multi-label learning-based emotions methodology in OSN s. Experimental tests based on a dataset with human annotations are conducted, and the findings demonstrate that the suggestedmethodology can outperform the most recent techniques.
Rapid Video Deduplication based on Global &amp; Local Features using Convolution Neural Network

Video deduplication faces significant difficulties because of the large video data explosion. In recent years, deduplication algorithms have made encouraging strides, particularly with the aid of binary hashing techniques. However, research on the general hash-based architecture and the effective similarity ranking technique for video deduplication has been sparse up to this point. The existing hashing techniques finds duplication of videos in the form of binary hash at file level i.e., entire video (Global feature comparison). The proposed research focuses on developing a versatile and quick video deduplication methodology which checks duplication of videos at frame level (Local feature Comparison). If the input video is not entirely duplicated (global feature extraction), the video is then divided into frames to check duplication at frame level. To achieve this, the power of Deep Learning is exploited, using CNN, and a distinct compressed representation for each frame is produced, there are a little over 1000 features extracted for every frame and stored locally. The performance of the proposed methodology is measured using the Euclidean distance measure, that calculates the distance between the pre-trained data and the query video. The goal of the proposed local feature extraction is to assess the number of duplicate frames over the entire video. The proposed approach achieves an accuracy of 95.6% in finding out the number of duplicate frames and it has been concluded that it performs the best when compared with other state-of-art algorithms.
Voice Controlled Wheelchair

In the moment's world, being independent is veritably essential to survive. Unfortunately, this isn't possiblefor physically impaired people. They're constantly dependent on backing to move around freely. The proposed system provides an advantage to these people by allowing them to carry out their diurnal conditioning singly. This system uses an ESP32 Microcontroller which has an in-built Wi-Fi module. This allows the stoner to move the wheelchair using voice commands through an app, which rules out the operation of any backing. Also, the stoner's device is similar to a mobile phone, or tablet, etc. can also be charged as the wheelchair operates.
Parallelization Algorithm of Computer Vision for Autonomous Vehicles

The important features that enable computer vision in autonomous vehicle technology and infotainment function are image processing and object identification. Image segmentation is the preliminary step of any image processing algorithm. This work uses the Union-Find algorithm to segment images on a grey scale. It is a simple yet effective algorithm for intense applications. A review of the image segmentation algorithms and parallelization techniques is presented in this paper. Initially, three different profiling techniques are applied in order to identify the hot-spots, i.e. most time-consuming parts of the code. Parallelization techniques are applied to the regions of the hot-spots identified during profiling. The Union-Find algorithm is parallelized using OpenMP, MPI, and CUDA. The execution time decreases with the increase in the number of threads till a certain optimal value of threads. The optimal number of threads is found for the respective parallelization techniques.
Improving user Authentication and Face Identification for Android Application

Now a day, using mobile cameras can do numerous types of picture recognition. Face detection is one of these recognition techniques that is widely utilized nowadays. People are regularly tagged the pictures, that we post on social media. The Kinect concept is also employed in video games and security to provide only authorized people access to exclusive places. These are just a handful of current applications for facial recognition; the technology is essentially everywhere. This paper aims to develop smartphone software that can recognize faces. Controlling who has access to particular spaces or rooms is the major objective of this application. As instances, consider medical facilities or educational institutions where certain staff members are only allowed in specific areas. Of course, there are many applications for this kind of technology.
Enabling Secured and Seamless Crypto Wallets: A Blockchain Solution

A cryptocurrency wallet (crypto wallet), similar to a virtual bank account, allows users to store, send, and receive digital currency while also tracking their balance and transaction history. The user's private keys, which are required to access their digital assets, are protected. Crypto wallets are becoming more popular as they improve the user experience through improved interfaces and user-friendly designs. However, due to the increase in breaches and scams in the cryptocurrency market, security has risen to the top of wallet providers' priorities. As a result, several wallets now include enhanced security features such as two-factor authentication and biometric authentication. Low latency and lightweight crypto wallets are essential for quick and efficient bitcoin transactions. Low latency ensures quick confirmation of transactions, reducing the time required to complete a money transfer. Because they use fewer resources and are faster to download, install, and execute, lightweight wallets are more accessible and user-friendly, particularly for devices with limited storage and processing capacity. Blockchain technology is used in cutting-edge wallet solutions to provide unrivalled security while remaining computationally light. This positions blockchain as a crypto wallet facilitator, addressing the aforementioned challenges. The decentralised architecture of blockchain ensures the security and preservation of cryptographic assets. The distributed ledger speeds up processing, and light nodes keep the cryptocurrency wallet small.
Analysing Of Image Quality Computation Models Through Convolutional Neural Network

Through the use of several well-known characteristics of the human visual system, objective approaches for evaluating perceptual picture quality have historically attempted to quantify the visibility of defects (differences) between a distorted image and a reference image. It offers a different complementary framework for quality assessment based on the CNN under the premise that human visual perception is highly adapted for obtaining structural information from a scene. When an image is compared to its own blurry image, the CNN algorithm's goal is to offer information on the image's clarity. It uses the FFT results and the individual picture sharpness values for this. When contrasted against a blurry version of itself, the CNN algorithm's goal is to reveal information about the image's clarity. This will be done by using the sharpness values for each image as well as the FFT values for the photos. It is practical to use the Fast Fourier Transform technique. FFT is a tool for image processing that may represent an image in the spatial and Fourier domains. The image is thus represented by the FFT in both real and fictitious components. Thiscan-do image processingoperations like blurring, edge detection, thresholding, texture analysis, and even blur detection, by looking at these numbers.
Breast Cancer Prediction Using Unsupervised Learning Technique K-Means Clustering Algorithm

Breast cancer is a highly deadly and common cancer that is spreading over the world and claiming many lives. Breast cancer develops in the glandular tissue of the breast in the lining of epithelial cells of ducts or (15%) lobules. These tissues heal with time. These in situ tumours may develop over time and infect the breast cells' immediate environs (stage 1), impact the lymph nodes, and finally totally spread throughout the body's organs (stage 3). There were 685,000 deaths worldwide in 2020 and a later rise in the number of malignant women. Cancer was a common disease that affected 7.8 million of the female population. The k-means algorithm is a popular data clustering algorithm. As the main analytical routine in data mining, the techniques of the clustering algorithm will impact the clustering outcome directly. This paper examines the shortcomings of the standard k-means algorithm and discusses them. This paper reviews existing methods for selecting the number of clusters for the algorithm. However, one of its drawbacks is the requirement that the number of clusters, K, be specified before the algorithm is applied. Therefore, we obtained an accuracy of 85% after executing the program, and we were able to depict the difference between a benign and a malignant tumour. And we were able to see the centroid between the two tumours.
Simulation of a Wheeled Robot using ROS

Simulators are the tools used to study the behaviour of an entity, in this case, Wheeled Mobile Robots. These simulators played a significant role in robotics research as a tool for effectively testing algorithms, concepts and strategies. One such widely used simulator in the field of robotics is Gazebo. This paper overviews how a differential drive robot is simulated using Gazebo in a ROS environment. ROS is an open-source robot operating system. As ROS is open-source, many people have contributed to building robot software systems. These software systems can be used for various research and other requirements accordingly. Gazebo enables Simulation of the world, physical model, sensors and control system through the Unified Robot Description Format (URDF) file. ROS is interfaced with Gazebo to allow the implementation and usage of various robotic software systems and tools on the simulated Robot. In this paper, Localisation, Mapping and Path Planning algorithms are being implemented on the mobile Robot.
Classification and Segmentation of Marine Related Remote Sensing Imagery Data Using Deep Learning

Ship monitoring plays a crucial role in maritime safety, port administration, Ship traffic, maritime emergency and national defense. Using object detection methods U-Net and YOLOv2, image-based Ship detection has been put into practice but these methods have limitations, in U-Net at runtime, we can run the image only once which reduces its speed and in YOLOv2 all the anchor boxes are of same size, so objects with various sizes and shapes are difficult to detect. Hence to solve these issues a proposal with better techniques like YOLOv3 has been used to detect objects with super speed and various sizes of objects with the help of anchor boxes and U-net, which only requires a small number of training samples but offers high results for segmentation tasks due to its usage of a loss function for each pixel in the input image, this allows for simple identification of specific cells within the segmentation map. Hence the performance of these algorithms is measured to determine which of these two algorithms has more accuracy.
Congestion Management: Using Deep transfer learning for Traffic Classification, Layer4 forwarding, OpenFlow meter

Network traffic classification methods are used for congestion management, QoS delivery, billing in ISPs, and security purposes in firewalls. In the past, port-based, data packet inspection, and traditional machine learning techniques have all been widely utilized. However, their accuracy has decreased as a result of the Internet traffic's tremendous changes. The training data and test data are assumed to have independent, identical distributions by deep learning models used for network traffic classification. Due to changes in traffic features, this assumption could be incorrect in actual traffic classification. The classification of new network traffic will fail to use the models that were trained on the existing data. In this research, a deep transfer learning model without the aforementioned assumption is provided. The deep transfer learning technique is used to transfer the knowledge learned by the pre-trained traffic classification model to another model that has a smaller dataset and computational resources. The transfer learning model is built on ConvlD and BiGRU hybrid models that can achieve 98% accuracy in a completely new traffic classification target domain. In this research, deep transfer learning techniques are used to get over resource constraints and construct models for classifying network traffic based on deep learning. After classifying, the packets are forwarded by the OpenFlow switches according to the flow table configuration. Also, packets are forwarded by using Layer4 forwarding and OpenFlow metering methods for optimal bandwidth allocation to avoid network traffic congestion.
Design and Simulation of an Autonomous Mobile Robot(ScoBot): Locomotion, Perception, Navigation

Mobile robots are extensively employed for several industrial, research, and consumer services tasks. This paper proposes the design and simulation of an Autonomous Mobile Robot (AMR) named “ScoBot (abbreviation for Scorpion Robot)”. It is a differential drive-based mobile robot whose task is to autonomously navigate the environment to traverse from one place to another without human intervention (i.e., Locomotion). At the same time, avoid the obstacles placed in the environment (i.e., Perception task) with the help of an onboard LIDAR sensor. The Adaptive Monte Carlo Localisation (AMCL) algorithm is employed for the robot's localisation. The path planning operations are handled with the help of a local planner (Dynamic-Window Analysis approach) and a global planner (Dijkstra algorithm). The design of the robot is carried out using 3D CAD modelling software. It is exported as a URDF(Universal Robot Description Format) file so that all the above-said tasks on the simulation of the autonomous motion capabilities of the robot are employed in ROS(Robotic Operating System) and Gazebo environments. Finally, the results of the simulations are observed both in the Gazebo and the live actions/ working of the employed algorithms are monitored through Rviz (ROS visualisation).
Real-Time Sign Language Detection

Lack of communication or miscommunication brought on by linguistic problems can lead to awkward situations in today's culture. Those who are deaf or have trouble hearing use sign language, a visual form of communication, to communicate. It communicates meaning through hand gestures and body language. Yet not everyone is able to interpret sign language, which might result in miscommunication. A system was developed employing cutting-edge technologies to address this problem, including deep learning, machine learning, convolutional neural networks, computer vision, TensorFlow, and Python. This technology is made to accurately detect and identify sign language motions in real-time. The system develops a real-time sign language recognition tool using OpenCV. The 26 letters in American Sign were categorised using a CNN classifier. The use of technology to bridge communication gaps and create inclusive environments is crucial in our society. This system's high accuracy rate is an excellent indication of its reliability, providing a promising solution for individuals who use sign language to communicate. It would be interesting to learn more about the specific CNN classifier used in the project and how it was trained to recognize ASL gestures. Overall, the implementation of this technology can create more inclusive and accessible environments, ensuring that everyone can communicate effectively, regardless of their abilities or differences.
SEEMA: An Automation Framework for Vulnerability Assessement and Penetration Testing

The world has witnessed an exponential increase in the number of Internet Connected Computers due to various causes. This exponential increase and the resulting dependence have had far reaching cybersecurity implications. The primary implication being exponential rise in cybercrime which necessitates the vulnerability assessment and penetration testing (VAPT). The vulnerability assessment and penetration testing aids in offsetting any liability associated with a successful attack on an organization's asset. The current frameworks and tool available for VAPT require very high expert knowledge and are predominantly manual. Moreover, some tools available automate only certain phases of VAPT but not the whole process. This mismatch provides the opportunity to create a solution by automating the whole process while lowering the knowledge threshold. Thus, this paper provides a revamped automated framework for a novice individual and a cybersecurity professional's use. It also provides a framework for commercial use which leverages multi-agent system and certain aspects of full stack monitoring.
Real-time Object Detection for Enhanced Security: Implementing YOLO Technology

The primary goal of this project is to offer a tool for bank security. As a result, a large number of people who may or may not know us are using the personal workplace. The loss of assets and significant paperwork is one of several issues. Even the most advanced technology, such as a fingerprint sensor lock, may be unlocked with ease. To address this problem, we will thus develop an AI based security room and locker system. Project included using artificial intelligence to track users with the aid of our evolving project. We reduce the time it takes to recognise many items while preserving the greatest level of temporal complexity in the proposed research by using local binary patterns (LBPH) and machine learning techniques. To ensure that only those with the proper authorization may open the door, this system is interfaced with the hardware system run by the ATMEGA328P microcontroller.
A Novel Method for Image Inpainting Using CGAN

Image Inpainting can be defined as the task of filling out blank spaces or holes that may appear on images. It has been mostly used for restoring old and damaged photos. Traditionally methods like image segmentation and auto encoders were used to obtain results for this problem. Also, the old traditional methods had to have datasets where the developer would manually pick which areas for the model to train so that it can paint those missing regions correctly. Newer deep learning-based techniques, which may also be carried out unsupervised, have demonstrated remarkable performance in producing visually realistic and refined contents for the missing regions in free-form image inpainting challenges. Although several existing solutions concentrate on including more inputs to address this issue, it is difficult to directly apply cutting-edge inpainting techniques to image extension since they frequently produce blurry or repeating pixels with erratic semantics. The discriminator of a generative adversarial network (GAN) is subjected to semantic conditioning, and the findings on image extension with coherent semantics and aesthetically pleasant colors and textures are strong. We also present encouraging findings for long extensions.
Temple Inscriptions Recognition and Transliteration in Devanagari Script

Ancient inscriptions, palm scripts, manuscripts, etc., have vital information about India's rich culture. Recognition and understanding of these inscriptions have been challenging for epigraphers and professionals. The goal of the proposed research is to advance optical character recognition methods for archival Vatteluttu script inscriptions, which date back to the 4th or 5th century AD. This paper discusses a deep learning model to transliterate the ancient Tamil inscriptions (Vatteluttu Script), which can be extended further to other languages. The proposed work is beneficial to epigraphists, archaeological researchers, and the general public who are interested in this topic. The developed deep learning model has achieved an accuracy of 84.12%.
Performance Analysis of Real and Complex Wavelet Transform Techniques used for Wavelet-Based Image Denoising

Image de noising is a principal technique majorly used for original image restoration, segmentation and image classification. It is basically used to refine the images by eliminating noise embedded. In the current work, authors present a denoising technique based on Wavelet Domain Filtering. Denoising of images after domain transform helps in separating the noise and data components. The discrete wavelet transform and dual tree complex wavelet transforms work on the analysis and synthesis filter banks to filter and further segment the noisy input signal to low frequency and high frequency components constituting data artifacts and noise respectively. The progressive decomposition of data to a particular number of levels finally results in a noise-free output after filtering, considering a particular threshold. A comparative analysis of thresholding techniques is presented and evaluated for the parameters Signal to Noise Ratio (SNR) and lowest Root Mean Square Error Value (RMSE). The simulation results indicate superior performance of dual tree complex wavelet transform(DTCWT) when compared to the discrete wavelet transform.
Decentralized User Identity Management using Blockchain

When it comes to identity, there have been issues with security, privacy and usability for a long time. With the growth of the internet and tremendous amounts of data, the need to shift to a more apt system becomes apparent. These concerns lead to the question of our identity online and this is where Identity Management Systems(IdMs) come in. A lot of studies focus on the benefits of IdMs on the blockchain, these include but are not limited to traceability, transparency, security, and scalability. Identity management is a long and tedious process as of now, in this study different existing solutions have been reviewed, and solutions dealing with decentralized IdMs and provide an implementation of a stable, secure, and scalable Decentralized Identity Management system.
Photographic Attendance Tracking System Online (PATSO)

Attendance tracking is critical to monitoring student or employee attendance in academic and organizational settings. With the rise of online education and remote work, traditional attendance tracking methods are becoming less effective. This paper proposes a web application for an image- based online attendance tracking system (PATSO) that utilizes machine learning algorithm SVM for classification and face recognition technique to identify and track attendance. The system gets a group or an individual image of students from the user and compares them to a pre-stored database of images to verify attendance. The proposed method is convenient, and accurate, and eliminates the need for manual attendance tracking not only in online mode but also in offline modes. The system's effectiveness was evaluated through a series of experiments, which showed a high accuracy rate of over 90%.