Reviewerly/exHarmony huggingface dataset:
A Framework for Hate Speech Detection using Different ML Algorithms

Social media is one among the most widely used platforms in the world. People use social media for communication and many other purposes. However, this has led to a number of issues, including the proliferation of hate messages. It is becoming increasingly difficult to identify hate speech. While there have been some completed works on this topic, research works still need to be improved in terms of accuracy. To address this problem on social media, this study proposes various ML methods to mark hateful contents on various datasets. Text classification tasks are commonly used to model hate speech concerns. This study makes a contribution by combining features extracted through feature engineering techniques and comparing the performance of ML algorithms on publicly available dataset with three different categories.
Implementation of Real-Time Object Detection on FPGA

Object detection is a common and challenging problem in today's world. Also due to the rapid development of deep learning employing underlying deep models over the past ten years, many researchers have investigated and contributed to improving the performance of object identification and associated tasks such as object classification, localization, and segmentation. Inference time and detection accuracy are used to rate the effectiveness of any object detector. In terms of detection accuracy, it is observed that the two-stage detectors outperform the single-stage detector however, single-stage detectors outperform their rivals in terms of inference time. Additionally, You Only Look Once (YOLO) and its versions have improved detection accuracy, some-times surpassing two-stage detectors. In this study, we performed a co-design methodology for hardware and software (HW/SW) aimed at Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) based heterogeneous platform which is Xilinx FPGA Kria KV260. A CNN-based algorithm is first extended to the YOLOv4, v5L, and v5s framework before being implemented on the Kria KV260, an out-of-the-box platform for developing advanced vision applications. A reduced computation time is observed for the deployment of the Machine Learning model on Kria KV260.
Cloud based Analysis and Testing of Operations in a Flight System using Embedded Sensors and Microcontrollers

The process of flight testing is an essential part of flight operations. These tests are conducted over a period, and only after the results are as per the expectations can such a system be put into practical use. The whole process of testing involves specifying the parameters of the flight system, analysing a model prepared for the design, performing tests on the model, producing the system, and, finally, testing it in actual flight conditions. This flight-testing procedure will check the fundamental functionality of the aircraft's systems, gauge engine output, and assess the stability of the dynamic system. It will also evaluate the flight controls. The airplane is prepared for take-off once all necessary testing has been finished. Data from flight tests are precious and serve as the cornerstone of civil aircraft research and development. This research looks at the urgency and requirement of developing an aircraft data management system. The design considerations and conceptual framework of a flight test database system integrated with a multisite-based distributed accessibility and operating system are offered by analysing the current state of relevant techniques and requirements for a test flight data management system. Then, a description of the structure, functional components, and application follows.
Underground Cable Fault Detector with Distance Locator

A disruption or break in a cable that is buried underground is referred to as an underground cable fault. A few things, including normal wear and tear, damage from digging, moisture, temperature changes, or other external variables, can result in cable problems. The process of tracing and fixing cable problems can be difficult since it calls for particular tools and knowledge. The objective of this research study is to find the underground cable lines faults. Phase and distance are used to analyse how well the suggested model works. The distance and phase of the fault are shown on a 16#2 Liquid crystal display that is connected to the ArdunioMega. The proposed model eventually performs better in terms of time.
A Novel Decentralized Product Verification using Blockchain Technology

A blockchain is a distributed database that serves as a public ledger of all digital events and transactions executed and shared among participating parties. Consensus from a majority of system participants verifies each transaction in the public ledger, and once recorded, the information cannot be erased. The blockchain contains a verifiable record of every transaction ever made, making it similar to a cookie jar in a crowded marketplace compared to a secluded location. The product verification system utilizes the Ethereum blockchain to deploy smart contracts in blocks. There are two users: manufacturers and customers. Manufacturers can save product information, including product price, name, manufacturing date, location, and expiry date, in the blockchain as a smart contract. This information can be saved in QR codes and NFC tags that act as digital invoices for the product. Customers can confirm product details by scanning the QR code or NFC tag. Smart contracts are built using Solidity, and the deployment is completed using the Truffle blockchain deployment tool. Firebase handles authentication capabilities, allowing analytics on the number of authenticated customers.
Map-Reduce Task Scheduling Optimization Techniques: A Comparative Study

Cloud computing is becoming popular because it offers the pay-as-per-use model with business benefits of enhanced scalability, flexibility, mobility and reliability with cost reduction in use of required resources. Multiple map-reduce sub-tasks from different users use the cloud resources simultaneously. Scheduling these multiple tasks to the available resources is quite challenging. Without optimized scheduling, desired quality of service cannot be achieved. With effective scheduling of map-reduce sub-tasks other parameters of interest like cost, energy consumption and resource usage can be optimized as well. It is obvious that task scheduling optimization on cloud attracted a lot of researchers. Many heuristics, meta-heuristic and their hybridizations were used for scheduling the multiple tasks to available and limited resources of cloud. Use of metaheuristic algorithms in map-reduce task scheduling on cloud showed better results than only heuristic algorithms. Researchers were well realized that by combining the useful features of two or more algorithms, best scheduling solutions can be achieved. Different available hybrid algorithms are reviewed in this study. It will classify the available hybrid algorithms on different parameters for business benefits. it provides the future directions for hybridization of meta-heuristic algorithms for map-reduce task scheduling optimization. In this review different existing models for map-reduce task scheduling optimization in terms of make-span have been explored.
A Detailed Review on Object Detection Algorithms

Nowadays., object detection has become very crucial in the area of computer vision. Many day-to-day activities require the use of technology that can help in vigilance such as traffic rules violations., road safety., etc. The detection techniques work on the images or videos and act as a model that provide the required area of interest from that input media. To solve these existing problems., different algorithms are available to perform object detection. This study focuses on reviewing the available algorithms to assist in the detection of object based on time and accuracy. The end result will help to identify the best available algorithm that can achieve faster object detection. The algorithms taken for the review process are CNN (Convolutional Neural Networks)., RCNN., Fast CNN., Faster RCNN., Single shot., YOLO (You Only Look Once).
Advanced Chatbots for Home Patients using AI

Artificial intelligence can be used to respond to chatbots' questions through textual or spoken interaction. Chatbots can be taught to answer consistently, differently to messages containing particular terms, or even utilize machine learning to modify their answers based on the circumstances. In any event, there is still skepticism about the employment of artificial intelligence in a field where livelihoods could be at risk. It is debatable if human employees should be given the aforementioned responsibility. This medical chatbot program assist sufferers by offering round-the clock virtual better healthcare and responding to specific and general inquiries. Additionally, it assists in the development of prospects and automatically sends accurate findings to advertising. Through posing the queries in the right order, you can help individuals by pointing them in the direction they need to go. Such AI systems interact with service users who are browsing the website and assist them in locating experts, scheduling appointments, and obtaining the proper care. The model can be trained using a monitored ANN technique for machine learning using datasets that contain data file for various diseases. The objective is to assess the outputs of different designs and determine the most appropriate model to apply for symptoms in inputs from the actual world.
Blockchain-IoT Layered Architecture, Current Trends, Challenges, and Applications

The term “Internet of Things” (IoT) refers to the potential global connectivity of billions of individually networked devices that can collect and share data. This connectivity might occur everywhere on the globe. By combining IoT with Blockchain (BC), a robust and secure system might be created to store information gathered by intelligent gadgets. Internet-connected IoT devices can upload transaction data to private BC networks. As a result, all transactions in such networks are recorded in immutable ledgers. In order to comprehend how devices may connect with one another, this study examines the interplay between BC technology and IoTs. In this article, a BC layered architecture as a practical framework for integrating BC technology with IoT by making use of state-of-the-art tools and methods is proposed. Current trends and difficulties, as well as potential application domains, are discussed which is in relation to the layered architecture of Blockchain technology.
Generation of Hilarious Animated Characters using GAN

Now a days creating cartoon characters having animated features is quite interesting and challenging. It takes a lot of effort to sketch animated features to bring it to live. It takes time and requires specialized knowledge. It takes a lot of effort to build a cartoon character from scratch, and the end result is usually an ungainly design with low polygon intensity. This process can be simplified with the use of generative adversarial network (GAN) architecture, GAN can be trained using a library of cartoon images. GANs are made up of two networks: a generating network and a discriminator network. GANs have a lot of potential in the field of image processing due to deep networks' proclivity to create realistic images and the competitiveness of the training approach. This strategy appears to be a powerful tool for improving fuzzy photos. The basic concept underlying GAN is that it consists of a discriminator and a generator, two neural networks that compete in a zero-sum game.
Real and Synthetic Underwater Image Enhancement by Colour Compensation with Complementary Colours

Underwater images undergo many light effects which degrade the quality of the image. Underwater images suffer from scattering and attenuation of light in underwater environment. Attenuation of light signal in underwater environment vary from colour to colour which complicates the process of identifying light attenuation and also enhancement or restoration. The combined effect of light attenuation and scattering will pose a difficult situation for capturing underwater images. Most of the underwater image enhancement methods depend on depth estimation and strive to calculate depth of individual pixel. Illumination of depth in not only underwater but in any image is rather difficult and subsequent processes are mostly inaccurate. In the current work, a scheme based on white balancing is proposed. First, underwater image is compensated for colour cast and two images are generated from the original image by sharpening and Gamma correction in spatial domain. In wavelet domain, these two images are fused to generate high quality enhanced image. Simulation results along with enhancement metrics prove that the proposed scheme performs well on both real underwater images and synthetic underwater images.
Computer Vision based Avalanche Monitoring and Warning System

Avalanches due to excessive rainfall, typhoon and earthquakes are considered as major disasters in Taiwan. Avalanches cause severe threat to human life and property. Moreover, a large number of tourists attracted areas are under direct threat of avalanches which effects Taiwan tourism industry greatly. Therefore, developing an alert system for avalanches becomes inevitable. In this article, Taiwan's avalanche events are examined and have developed a computer vision based alert system to reduce disaster risks. This epic computer vision process uses a basic multi-criterion guiding technique to illustrate angle data and various highlights. When examining the diversity of coordinating traits and elements, the breeding results show benefits. The avalanche detection and warning system developed in this research can provide data for disaster management and policymakers to strengthen offices and institutions in general and further develop disaster risk reduction methods.
Drone Detection and Classification using Computer Vision

Drones, also known as Unmanned Aerial Vehicles (UAVs) are based on the principle of rotor torque pushing the air down which results in the upward lift of the drone. UAVs are used in tasks such as rescue operations and item delivery in remote areas, surveillance, agriculture, wildlife conservation, outer space and photography. Due to their low cost and high efficiency, it is used by diverse groups for both better and worse causes. The cases of malicious uses of military drones and spy drones employed are on a rise. The malicious activities deployed by the military drones may include air strikes at enemy military bases, army troops and in some cases end up causing the death of civilians in proximity. Drones that are used for espionage can retrieve valuable information regarding the different strategies of the military, can track the location of the army personnel and spy upon unsuspecting civilians. These drones are eliminated after being detected by the persons involved but sometimes in order to reduce risks, harmless delivery drones are also discarded. In order to aid against the malicious drone activity while making sure that unnecessary panic over the delivery drones and material-loss is not caused, a real-time computer vision system is proposed that can identify the drone in the given region of interest, give its relative location and classify the drone. The Convolutional Neural Network (CNN) architecture, You Only Look Once Algorithm (YOLOV5), is used to classify the drone into one of the categories: Army, Surveillance and Delivery drones.
Apparatus to Measure Interpupillary Distance and Near Point of Convergence

The distance between a person's two eyeballs is called their interpupillary distance. The proposed model eliminates the human error by automatically measuring the Interpupillary Distance and the Near Point of Convergence. In this research study, a T-shaped device is designed that contains an imaging unit as well as a processing unit. The image capture mechanism is set up to grab stills and videos of the subject's face while it is in the device's frame. Instrument also aids subject in performing test by providing audio instructions. The precision of the design can act as a second opinion tool for the doctors and researchers.
Blockchain based e-voting system

Voting is an important aspect for democratic countries. It is a process of expressing the choice or opinion of people on a particular matter. Voting can take place in a variety of contexts, such as in national or local elections, within organizations or associations, or in public surveys. Voting is a fundamental right in many democratic societies, and it allows citizens to participate in the governance of their country or community by selecting representatives or making decisions on important issues. It is an important way for people to have a say in the policies and laws that affect their lives, and to hold those in power accountable. Voting decide which candidate is capable and also decides the future of that country therefore voting should be as transparent as possible and should have high level of security. But the existing voting system have some flaws like Security concerns, Centralization, Limited Audibility etc. Due to this inclination, voters is decreasing towards voting and voting percentage decreases. There is a solution to these issues is strong requirement of a system which has High level of security. Transparency. Decentralization. etc.
Sign Language Translation in WebRTC Application

Communication has been an essential part of human life. Different languages are present around the world for communication. Still, people who have lost their hearing and speaking ability by accidents and genetic birth often face difficulty in communication. Auditory-impaired people have found sign language helpful in communicating with others. Hearing-impaired people must always have a personal interpreter available for translations whenever they need to communicate. People with disability of hearing impairment find it challenging to interact with others on social media and the internet to form new relationships on their own. An open-source video-conferencing application that can translate sign language is quite helpful for the hearing impaired. Sign language recognition (SLR) has drawn a lot of attention as a way to close the enormous communication gap. However, sign language is far more complex and unpredictable when compared to other activities, making it challenging for reliable recognition. The Speech-to-Text API enables speech-impaired people who can read to comprehend others. The Sign Language Translation Application (SLTA) allows them to communicate by translating their sign language into text that others can understand. The proposed method uses python, the MediaPipe Framework for gesture data extraction, and the Deep Gesture Recognition (DGR) Model to identify the sign motion in real-time. The proposed method achieves the highest accuracy of 98.81% using a neural network comprised of Long-Short Term Memory units for sequence identification.
Analysing the ML and DL-based Models for Predicting Malarial Fever Prior to Clinical Trial

Malaria is a deadly disease caused by unicellular protozoan parasites of the Plasmodium genus. This disease is widespread throughout the world. Confirming the presence of parasites early on in all cases of malaria allows for the administration of species-specific antimalarial medication, which reduces mortality and points to other illnesses when the diagnosis is negative. Nonetheless, light microscopy of thin and thick PB films stained with May-Grünwald-Giemsa (MGG) remains the gold standard. Because this is a labor-intensive process that relies on the expertise of a pathologist, medical professionals in areas of the world where malaria is not common may have difficulty diagnosing cases of the disease. This study used thirteen different machine-learning models to predict malaria fever. The Gaussian NB, Logistic Regression, XGB, Bagging Classifier, Random Forest Classifier, Extra Trees Classifier, Gradient Boosting Classifier, Hist Gradient Boosting Classifier, LGBM Classifier, Decision Tree Classifier, Ada Boost Classifier, SGD Classifier, and K Nearest Neighbors (KNN) Classifier were among the models used. This study classifies malaria cases using data obtained from chest X-ray images for this study. A dataset with 1079 patient records and 23 attributes was created. These characteristics were obtained from the Kaggle repository. Out of these 23 attributes, 80% of the data were used to train the model, and the remaining 20% were used to assess the validation accuracy. It has been shown that the Gaussian NB models were the most accurate, with a 97.66% accuracy rate.
Blockchain-based Construction Management System

Trust is one of the most significant substantial assets in every organization especially when it comes to the construction industry which involves a dynamic clustering of several firms integrating various stages of a construction process from planning to implementing where each phase is complicated and a part of a more complex overarching process. And hence this is a sector, particularly where centralization is unnatural, and privacy is of utmost importance. Especially, when communication patterns among participants show a peer-to-peer nature of the relationships. In such an environment, Blockchain can provide a trustworthy infrastructure for information management during all building life-cycle stages. Hence, the end goal is to develop an application that pools information to share data between parties with the help of Blockchain technology that solves the collaboration problem, provides a common platform to achieve minimalist paperwork, as well as maintains the authenticity of the information handed by the participants by validating different users (Builders, Contractors, Client) to keep up the credibility of the knowledge provided by each party.
Evaluation for Optical Character Recognition of Mobile Application

The library's ancient books are deteriorating, due to faded ink, and damaged paper. It is challenging for librarians to both manage and discover information. Therefore, a technique to overcome this is required, such as implementing text recognition by leveraging many IOT -based OCR apps in mobile phones, such as Text Fairy and Camscanner to find out the results of the effectiveness of OCR mobile apps. Accuracy calculations are needed, in this case, the use of CER and WER is implemented to determine the accuracy. From the results of CER and WER calculations, it was found that in book cover data, Camscanner comes out better than Text Fairy. However, on data that is entirely text, Text Fairy is better when compared to Camscanner. It can be concluded that Text Fairy is superior in recognizing text-only parts, whilst Camscanner is superior at recognizing parts that are not entirely text.