Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps

In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.
An Enhanced Intelligent Algorithm on Fault Location System

With the rapid development of modern society and the gradual improvement of people's living standards, the society and individuals have put forward higher requirements for safety and reliability of power supply. Therefore, the development of power distribution automation system as one of the important ways to improve the safety and reliability of power supply, when the fault occurs, the feeder terminal can report the fault information to the master station; then the master station, according to the corresponding information reported, uses the corresponding algorithm to detect the fault location quickly and accurately, and isolate it. For the non-fault power outage area, power supply is restored to reduce the loss of production and life. Therefore, studying the fault location and isolation technology for distribution network is very important to improve the reliability of distribution network. At present, the research on the fault location of smart grid has made some progress, and many scholars have proposed different programs respectively. In a published research, the developed software system can realize the information interaction among Energy Management System (EMS), Supervisory Control and Data Acquisition (SCADA) and Fault Information System (FIS); when complicated and rare faults occur, it can support operators to determine and change the fault component rapidly, so as to shorten the handling time of accidents and improve the efficiency of accident handling. In another study, the researchers improved particle swarm optimization for wavelet neural networks, and used the improved method for distribution network fault location, providing an important reference for the design and research of practical fault location system.
Compiling with MultiCores

Technology is evolving continuously at an exponential rate in terms of its performance. Traditional processors just have one core, however their computational capacity has been outgrown. Increasing the single core’s clock frequency is one technique to boost the system’s performance. A multitude of problems, including excessive power dissipation and CPU overheating, will arise when the frequency is increased. This issue was resolved by adding more cores to the same CPU to increase processing speed. Multicore processors are the term used to describe this architectural breakthrough. Each core functions independently. Due to these modifications, programmers are forced to code in parallel. We must create code that leverages several cores in parallel. The parallel programming paradigm was developed to create such programmes. The transition from sequential to parallel execution in the software development industry has been greatly influenced by parallel processing. We have OpneMP and MPI constructs, which may be utilised with current languages to parallelize the code.
Posture Monitoring Apparatus For Physiotherapy

The recent advancement in technology is leading to the emergence of a new lifestyle. Even though people are being assisted by devices from even small tasks in daily work has made the lifestyle easier but has also caused a reduction in daily activities and movements causing adverse health effects. Physiotherapy is a healthcare profession that is concerned with the functioning of humans and one's movement which will maximize the physical potential of the body. Monitoring the physical movements of the body is generally not easy but if it comes to monitoring movements of the muscles joints or spines it is very difficult without straining the body. Based on the physical conditions of the patients the physiotherapist suggests exercises that will help them to reduce the pain. The physiotherapist uses traditional methods like goniometry in which the angle between the joints is measured to analyze the pain in the muscles or joints. Also, they use the gait analysis method to analyze the balance in the body. Based on the analysis they suggest necessary exercises which are mandatory to monitor at home in order to reduce unnecessary straining of muscles. So we have proposed a solution which is a system that will monitor the exercises done at home with data provided by physiotherapy at once. The system will take the data of the exercise which were performed in front of the physiotherapist and this data will be saved by the system. This data will now be referred by the patient while performing the exercises at home and will monitor the movements of patients. The indication will be given if the movement is desirable or undesirable. The efficacy of home-based physiotherapy depends on the correct and systematic execution of prescribed exercises. Feedback systems enable it to accurately track exercise execution and prevent patients from unconsciously introducing incorrect postures or improper muscular loads on the prescribed exercises. This system will have Bluetooth connectivity which will help in transferring the data. This system is interfaced with the app which while performing will give the necessary outputs to achieve the best postural position in order to avoid unnecessary straining.
Insight on Human Activity Recognition Using the Deep Learning Approach

This work proposes a video understanding technique that primarily focuses on the individual action recognition appearing in the video. The state-of-the-art showed promising work in video understanding. Though, it's essential to require inclusive information on human action in real-time CCTV video surveillance, sports video analysis, health care, etc. This paper proposed a transfer learning deep neural network model designed for recognizing individual actions accomplished by multiple people in a video sequence. This research established a deep model which uses Region-Of-Interest (RoI) pooling layer to capture automated features from a specified video frame to recognize individual actions. The MobileNet model accomplishes this as the backbone to recognize individual actions from each video frame. The accuracy score of the model was compared with the CNN models VGG-19,InceptionV3, and MobileNet. The MobileNet is computationally low-cost and enhances the performance of individual action recognition performed by multiple humans in a video frame. The investigational results were evaluated by varying learning parameters, and optimizer of deep neural network. The experimental results of the proposed model for individual action recognition demonstrate the improved efficiency of the standard benchmark collective activity dataset. This research illustrates the progress of action recognition by employing the transfer learning CNN model along with RoI pooling layer.
Design and Implementation of Crime Prediction Using Machine Learning Algorithm

A common approach to reducing crime in a city is to encourage citizen-police cooperation. In the United States and around the world, Twitter is a popular social media platform used by millions of people. Predictive analytics based on Twitter can aid decision support systems. All messages are tagged in real time with their location and time of transmission, which is completely free. Machine learning techniques were used to create an intelligent method of effective criminal detection. In order to find the most accurate classifier, six different classification methods were devised. The learning model parameters of the classifier had to be carefully chosen in order to accurately predict the model's performance. The SVM (2.697) and XG-Boost (2.561) performed better than the other six classifiers in the overall log loss test.
Denoising of SAR Images using Wavelet Transforms and Wiener Filter

Noise is unwanted signal present in the image. Speckle noise is usually present in Synthetic Aperture Radar (SAR), ultrasound or any active radar sensor images. This noise limits the information interpretation. The proposed novel method is realized by first applying frequency domain methods for high frequency noise removal and then applying spatial domain filters. We have demonstrated various transform-based methods. Frequency domain methods gives ease to use separate bands for processing. Stationary Wavelet transform based method proves to be more efficient. Visual quality is improved as compared to the traditional speckle noise removal filters also the qualitative parameters like Peak Signal-to-Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Similarity Index Measure (SSIM) and Speckle Suppression Index (SSI) and Structural are improved.
Threat Detection and Rescue using Machine Learning

The present work aims to design and implement Digital Image Processing, which is a Numpy and LSTM- based detection kit. The combination has plenty of applications such as hand gesture recognition, sign language translators, home security systems, traffic control technologies, intruder recognition systems, etc. The current investigation utilizes the application for threat detection and rescue system. We have implemented a detection kit for hand gesture tracking and identification of the hand sign which signifies ‘threat’ that consists of two major units namely hardware and software. The hardware part comprises a camera that captures frames in the desired detection range. The detection and identification of the threat is recognized using the following software units such as Anaconda Navigator, Jupyter Notebook, NumPy library, TensorFlow, OpenCV, and MediaPipe. At first, the inputdata is trained for the key-points identification and then the camera captures the frame and compares it with the trained data set. When the captured key point which is hand-signed by the victim is similar to the key points of the trained data set, the threat is detected. The frames are re-evaluated thrice for an accurate result. This is then notified to the officials for rescue. The major advantage of this novel work is the less re-evaluation time and efficient frame process in a very short duration of time. It is also a low-cost system that produces a highly reliable result. This detection will certainly help to save a life and assure a protected life for people in society.
Automatic Question Generation from Textual data using NLP techniques

In this world of ever growing technology, humans have tried to develop systems to make their work as easy as possible. Education sector is no exception to this as we have got introduced to a lot of Ed-tech systems and can access any information or course just with the help of our smartphones. Carrying out educational activities is not an easy task. There are a lot of things to manage or taken care by the professors. To reduce the work load of the professors we have developed a Web Application to generate automatic questions based on the data being provided to the system. This will not only make the jobs of the professors easy but will also allow them to process huge educational syllabus and generate any type of questions using that data.
Anomaly Detection on Drone Forensic Timeline with Sigma Rules

Drones, also known as UAVs (unmanned aerial vehicles), are unmanned devices that provide unique functionality, enabling area surveillance, inspections, and surveys. In recent years, the rapid growth of drones has also raised several security concerns related to illegal activities, making them a source of evidence. Therefore, it is very important for digital forensic examiners to have the ability to analyze the source of content stored on drones. If the drone encounters a problem or has an accident, it is necessary to carry out a forensic analysis of the device. In this paper, we build a drone forensic timeline using the log2timeline plaso. This timeline records all drone activities. We then propose to apply Sigma rules to detect anomalies in the drone timeline. With this technique, digital forensic examiners can detect anomalous activities that occur on drones.
Alphabet Recognition using Air written Trajectories

The enormous potential use of air-writing recognition in intelligent systems has made it highly popular. Some of the most fundamental issues in isolated writing are yet to be fully addressed. Writing a linguistic character or word in free space using a finger, marker, or handheld device is referred to as a trajectory-based writing method. It can be used where traditional pen-up and pen-down writing techniques are inconvenient. It has a significant upper hand over the gesture-based approach due to its simple writing style. However, because of the diverse characters and writing styles, it is a difficult process. In this paper, an alphabet recognition system for alphabets written in air, where the alphabet is recognised based on air trajectories which are three-dimensional (3D) and gathered by a single camera in this study. A reliable and effective colour-based segmentation is proposed to extract air recorded trajectories gathered by a standard web camera,. This solves the problem of push-to-write by removing limits on users' writing without the usage of an illusory box. The trajectory is normalized for improved recognition using convolutional neural network (CNN). We achieve recognition in real time with a high accuracy of 95% and negligible neural network complexity. It beats and surpasses the currently used techniques that involvewritten images as input.
Image De-noising with Hexagonal Structures Using Adaptive Recursive Filter

Single-pass de-noising of images is often inefficient because the de-noised images always contain some or the other part of noise remnants from the single-pass operation. Thus, for true de-noising of the images, we need to re-apply the de-noising algorithm on the image, so that removes a remnant noise from the image and obtained the de-noised image. In this paper, we tend to propose a unique algorithmic rule that uses adaptive recursive algorithmic filtering using hexagonal structure to de-noise images that are debased by salt also pepper commotion. The proposed calculation is contrasted, and a traditional channel calculation based on a square structure. The results show that the planned formula improves the peak Signal to Noise ratio (PSNR) by quite 10 dB regarding typical filter using a structure of square.
Pattern Storage &amp; Recalling Using Hopfield Neural Network and HOG Feature Based SVM Classifier: An Experiment with Handwritten Odia Numerals

Handwritten Odia text identification is very useful for the society and also a challenging task as it may include a variety of writing styles, complicated character-touching, and abundant categories of character class. This work implements two different classifiers for handwritten Odia numerals. The first experiment involves the implementation of a Hopfield neural network with binary image as the feature set. Using the feature extraction technique based on the Histogram of Oriented Gradients (HOG), a Support Vector Machine (SVM) classifier is implemented in a second experiment. Different cell sizes have been used to extract HOG features and finally the best cell size with optimal features have been used. The data set has been created on Microsoft Paint by drawing the digits manually and also collected from different users. The Hopfield model has been implemented with four different data sets (0% noise, 10% noise, 20% noise and 30% noise). Addition of noise to the data has been done through programming. Both the models (Hop field and SVM) have been implemented through MATLAB and the data set has been used for testing the models. Results prove that the Hopfield model's performance is higher than the SVM classifier whereas, the recognition accuracy for Hopfield model is decreasing with increase in noise percentage and could be considered as poor performance.
Automatic Attendance System using a Group Image

Marking employee attendance and keeping track of it is crucial to any organization, and this record is used as a benchmark for many other things. With an enormous number of employees working there, physically recording each employee's daily attendance is tiresome and time-consuming. Digital solutions based on RFID, biometrics and other technologies have been developed. However, they require expensive hardware in-frastructure and ongoing maintenance, which isn't cost-effective. We present a revolutionary real-time attendance marking and recording system that addresses all of these problems to meet the demand of the digital age for attendance marking without spoofing or unethical behaviors. Our solution is a user-friendly android application that allows users to take and upload a group snapshot of the people whose attendance needs to be noted. The application has a robust backend that effectively recognizes each user by detecting their faces, extracting those faces, creating face embeddings, and comparing them to those in the database. We have recorded the recognition time of 8.28 seconds. Our solution has the potential to be quickly adopted by any company with just a few modifications.
Optimization of a Consensus Protocol in Blockchain-IoT Convergence

A consensus mechanism determines how partici-pants agree on a transaction or a state change in the distributed ledger technology (DLT) such as blockchain by receiving consent from majority of network participants before inclusion of a block to blockchain. The consensus mechanism is required in almost every blockchain application. Most contemporary blockchain consensus systems need massive processing capabilities, signif-icant energy consumption, and reliance on monetary stake. Because of these limitations, mainstream consensus approaches are unsuitable for low-resource applications such as IoT. As a result, this paper introduces a new consensus protocol known as “Delegated Proof of Accessibility” (DPoAC). The DPoAC protocol minimized the computational resource needs and energy usage demands of the existing proof-based consensus mechanisms, making it appropriate to use in applications with limited resources, such the Internet of Things. In addition, the lack of a financial stake in DPoAC makes it ideal for blockchain-IoT convergence.
Explicable AI for surveillance and interpretation of Coronavirus using X-ray imaging

Explainable AI (XAI) is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems. XAI is such a technology that could assist to alleviate the black-box system by providing new ways of understanding the core thinking process of AI systems. Conside ring the healthcare domain, doctors are still not able to explain why certain decisions or forecasts had been predicted by a particular system. As a result, it imposes limitations on how and where AI technology can be implemented. And to address this problem, a taxonomy of model interpretability is framed for conceptualizing the explainability. Also, an approach with the baseline system is created which could firstly differentiate in the Covid-19 positive and Covid-19 negative chest X-ray images and an automated explainable pipeline is designed using XAI technique. This technique shows that the model is interpretable, that is the achieved results are easy to understand and can encourage medicians and patients with transparent and reliable medical journey. This article aims to help people comprehend the necessity for Explainable AI, as well as the methodological approaches used in healthcare.
Application of Cloud Computing in an Education Sector through Education and Learning as a Service and its Cost Benefit Analysis

An education sector across the globe is facing numerous challenges. It is being one of the most badly affected sectors due to Covid-19. This paper presents a perspective on applying Cloud Computing technologies in the field of education at several abstraction levels. In addition, it proposes Education and Learning as a Service model and Decision-Making Matrix for an Organization.
Web Based Book Recommendation System Using Collaborative Filtering

Recommender systems are tools that help end users recommend products and obtain information about their preferences by going online. Today's online bookstores compete with each other in a variety of ways. One of the most powerful ways to efficiently increase revenue by attracting customers is a referral system. This study offers a clear, understandable method for recommending books that aids readers in selecting the best book. The proposed methodology works on training of database and feedback to provide meaningful information that helps users make decisions. In this paper recommendation system is developed by using collaborative filtering method. The machine learning (ML) model KNN is proposed to categorize the books as per user preferences. The overall architecture of the proposed system is introduced and its implementation is demonstrated.
The Smart Analytical Electricity Monitoring and Management Model for Commercial Industrial Purpose using Industrial IoT

It is no exaggeration to say that electricity has emerged as the sixth greatest force in this universe which has five forces namely water, earth, air, fire and sky. Electricity has become an essential part of human's daily needs. It seems that man can survive even without food but the situation has evolved that he cannot live without electricity. We rely mostly on electricity for the appliances we use on a daily basis. Electricity is a form of energy. Magnetic energy and electric energy are related to each other. All matter in the world is made up of small elementary elements that cannot be easily separated, and molecules are made up of smaller atoms. According to modern theory, current is the flow of electrons. So it is very important for us to know the carrier of electrons..
AI Enabled Blockchain solution for the Indian Judicial System

This paper outlines how the blockchain technology can be used in the Indian judicial system to remove some of the procedural complexities. It begins by exploring the complexities of the Indian judicial system and how blockchain with its immutability, trust, improved security, and traceability can transform the Indian judicial System for speedy delivery of justice. It seeks to examine the bottlenecks and complexities of the judicial system which are creating major hurdles in faster delivery of justice to common people. To address this gap this paper proposes a system model using blockchain coupled with Artificial Intelligence (AI) and ML can revitalize the Indian judicial system by eliminating the above issues to great extent. Blockchain with AI based analytics can also help the judges in accurate and faster delivery of judgements which will help reduce the burden on the higher courts. A combination of doctrinal, comparative, and empirical research methodology is used to evaluate use of Blockchain in the Indian Judicial System.