Reviewerly/exHarmony huggingface dataset:
MNIST Digit Classification using Parallelised Convolutional Neural Network

A program or piece of computer software is often built using serial computing techniques. In simple terms, a problem’s solution is created by breaking it down into smaller instructions, which are then each individually carried out by a computer’s Central Processing Unit (CPU). These modular instructions are first queued and then carried out one by one. Due to the fact that only one instruction was being performed at a time, this was a problem that could be plausibly argued to need to be solved in the computing industry. Hence, parallel computing is employed to benefit from serial computing. Using many processing elements simultaneously to solve a problem or carry out an instruction is known as parallel computing. Every operation is run or processed simultaneously, and problems are divided up into discrete instructions and solved that way. The number of Central Processing Units (CPU) in large-scale supercomputers is continually increasing, and parallelism is a key component of all modern supercomputer architectures. A deep learning technique called the Convolutional Neural Network (CNN) learns directly from the data. When dense layers are added, it transforms into a sizable polynomial time approximation technique. The Convolutional Neural Network (CNN) method will run faster and be greatly improved when it is parallelized. This strategy can be built on top of several convolution neural network -using applications. One such application that uses the convolution neural network technique to find numbers in images is MNIST classification.
A Survey on Natural Language Processing and its Applications

Advancements in technologies and the internet help to pave the way to collect and store unstructured data which includes voice, etc. The use of unstructured data in an efficient manner helps to understand the insight of the data and make a decision based on the data. To analyse the unstructured data, natural language processing (NLP) is applied. NLP referred to a subcategory of AI that mainly focus on computers understanding, and interpreting human being language. NLP is developed by combing different fields like linguistics, and computer science which helps the computer to perform as a human does in process in speech and text data. NLP is mostly applied in the application areas like content categorization, contextual extraction, sentiment analysis, document summarization, Machine translation, etc. The objective of the research is to perform an in-depth study about the NLP, its methodology, and applications area with pros and cons. Further, this survey helps new researchers to easily understand the underlying concepts of NLP and its terms.
Text Summarization for News Articles using Latent Semantic Analysis Technique

Text Summarization has long been an ongoing topic of discussion in academics. Even though different techniques for automatic text summarization have been developed in recent years, efficiency remains difficult to attain. In this decade, the automatic news summarizer is necessary for the size and quantity of documents available on online. The proposed system provides a text summary approach in this study that focuses on the challenge of recognizing the most significant parts of the text and providing cohesive summaries. There is no need to thoroughly examine the semantics of the text while using this technique. To construct summaries, it instead uses the Latent Semantic Analysis technique.
An Efficient High Throughput Block Chain Specification

Blockchain was thought of as a revolutionary platform with implementation potential in diverse industries. Thus, different blockchain networks have been implemented to analyze their utility in multiple scenarios. In this article, a comprehensive assessment of blockchain efficiency by classifying all the solutions into two broad categories; an empirical study and computational modeling is conducted. The latest methodological blockchain approach includes benchmarking, testing, experimental research and then modeling. It is comparatively tested for stochastic models of computational modeling which is used to test the efficiency of conventional blockchain consensus algorithms. By contrasting, comparing, and grouping the various approaches together, significant parameters can be used to choose the most effective measurement technology to maximize blockchain device performance based on the detection of the bottlenecks. Finally, the proposed approach lists suggested recommendations for possible inquiries.
A Machine Learning-based Automated Approach for Mining Customer Opinion

Amazon Reviews allows customers to review the purchased or used products. This type of user-generated content is incredibly helpful for e-commerce businesses; whether it is from existing customers or new ones, every review provides a valuable perspective and feedback on a certain product. Amazon product reviews are an incredibly powerful tool for both buyers and businesses in determining the perceived value of a product, informing decisions, and improving customer trust and loyalty. This study explores the use of machine learning-based methods to assess the sentiment of Amazon product reviews from a range of product categories, including electronics, home appliances, and fashion items. Through the use of Natural Language Processing (NLP) and deep learning algorithms, this study aims to determine whether there is a correlation between customer sentiment and product category. The analysis will further facilitate the development of a model that can accurately predict sentiment scores for any given batch of reviews or comments. According to the study, using Text Blob, Logistic Regression, SVM, and Multinomial Naive Bayes methods can improve classification task accuracy. It produced 91% accuracy with SVM and Logistic Regression and Multinomial Naive Bayes methods are 84% and 87% accuracy with on 4444 positive reviews and 469 negative reviews.
An XOR-based Multiple Medical Image Sharing using Visual Secret Sharing

On the basis of visual secret sharing, many researchers have proposed numerous techniques for image secret sharing. Most secret sharing techniques convert each secret image into multiple shares. Therefore, multiple shares are required for sharing multiple secret images. Such sharing technique uses large quantity of bandwidth and storage. This paper proposes a revolutionary Multiple Medical Image Sharing (MMIS) technique to reduce bandwidth and storage usage. This technique helps exchange multiple medical images as shares and performs reconstruction without any information loss. MMIS generates secret shares as public and private shares. Public shares are common for multiple images, and private shares differentiate individual secret images. A simple XOR operation is used to perform the lossless reconstruction of the initial secret medical image. As an individual’s medical data may contain multiple records, this property makes MMIS efficiently used for medical image secret sharing (each group corresponds to an individual). Compared to existing secret-sharing techniques, the experimental results show that MMIS reconstructs the images with better reconstruction quality. The MMIS security analysis shows that the technique possesses better security with minimum space complexity. Along with lossless reconstruction, MMIS offers different properties such as diversity to improve security and fewer shares to reduce bandwidth and storage. MMIS also ensures user authentication using private shares to prevent share abuse.
Face Recognition System using Artificial Intelligence: Comparison of Classifiers

Facial recognition is the technique used to identify the face of a person which is already detected and shows the results whether it is known or an unknown face. Face recognition is followed by the process of face detection. Both the processes are difficult tasks at their level. There are several methods or techniques to develop the system of face recognition, viz., Eigenface and Fisherface. The challenge for this system is that face images are with different backgrounds, different lighting, different facial expressions and occlusions. This system starts when an image is processed to train it. It is continued on the test image, the face is being identified, then the trained faces are compared and ultimately categorized it using classifiers of OpenCV. This study discusses the comparative study of different algorithms and come up with the most effective and convenient technique for the mentioned system.
Waste Management System with Technological Intervention: Advantages and Challenges

Waste management has become a critical issue due to an exponential increase in the worlds’ population. Inefficient treatment of waste disposal has led to health and environmental problems. As a result, the governments must find sustainable ways of waste management using technology. There are several technologies in Industry 4.0 aimed at tackling the existing problems of waste management. This study specifically discusses different models of waste management using AI and IoT. It includes an intelligent ecosystem for real time monitoring of waste management. Algorithms like Convolutional Neural Network (CNN) & Adaptive Neuro-Fuzzy Inference System (ANFIS), and IoT technologies like Radio Frequency Identification (RFID) & sensors have been discussed in this study. This is to refine and provide an accurate, efficient, and sustainable systems of waste management. This also assists in protecting the environment and health of the people and consequently achieving the United Nations Sustainable Development Goals. Lastly, this study provides recommendation and discusses limitations, suggesting improvements in the pre-existing architecture of waste management systems to boost their efficiency.
Scalable AR Integration Pipeline for Immersive Textbook Learning Experience

The availability of learning tools in schools that are accessible to low-income households is generally very limited when compared to most private schools that cater to the well-off masses. Contrarily, the accessibility, and usage of smartphones in the low-income population is surprisingly comparable to that of the medium and high-income populations due to the presence of affordable smartphones. Hence, enhancing the learning experience of children from low-income households through resourceful strategies that utilize smartphones can counter their school’s deficit of learning tools. One such strategy is proposed by this study, where an immersive and interactive learning experience can be created through the integration of Augmented-Reality based visualizations in textbooks that make concepts more appealing and clear. This approach is ideal for low-budget schools as it does not require any setup expenses from students or the school. This pipeline made scalability a key priority in order to accommodate a vast number of AR experiences that can cover an entire curriculum composed of different subjects.
Numeric-Digit Identifier based on Convolutional Neural Networks on Field-Programmable Gate Array

Convolutional Neural Networks (CNNs) are increasingly employed for voice recognition, image segmentation, and digit classification. Hardware support techniques are required as the need for processing power rises, and programmable hardware such as FPGAs are ideal for CNN workloads. The suggested approach is to recognize the digit using the MNIST dataset and hardware realization of CNN acceleration to minimize processing time and complexity. The is identified with 2 hidden and 2 convolution layers, developed in Verilog HDL for Xilinx Zynq 7Z020 FPGA with considerations for size, power, and logical consumption.
A Machine Learning Approach to predict the Next Word in a Statement

Next-Word Prediction (NWP), an application of machine learning also known as language modeling, is one the area of natural language processing that could forecast the next word in a sentence. Employing recurrent neural network models some researchers have previously implemented this NWP. In this work, two approaches using long-term memory (LSTM) model and Bidirectional LSTM were tested, running for 50 epochs and their accuracy and loss were compared. Web scraping technique was used for acquiring the data through various channels. Libraries like tensor flow, keras, NumPy and matplotlib were imbibed in the code to obtain the results. Tensor flow was used to download the model in json format and then Google Colab was employed to code the tool. Medium-articles-dataset was used here which after preprocessing, was trained using LSTM (Long short-term memory) and BiLSTM models which were then used to predict the word that comes next in a statement.
Security of Electronic Health Record using Attribute based Encryption on Cloud

Because of the likelihood of data breaches and the potential hazards of patient information, healthcare institutions face significant challenges in implementing cloud-based Electronic Health Record (EHR) systems. Current authorization models are based on a patientdriven approach to EHR system management, in which the patient’s agreement to allowing data access is prioritised. However, the patient has a considerable burden in ensuring the security of their healthcare record. Given the problems that healthcare providers confront, many employees are apprehensive to use cloud-based EHR systems. Data breaches and patient data compromise can result in serious legal and financial ramifications for healthcare organisations. To overcome these issues, healthcare organisations must implement more robust permission models that prioritise patient data protection and privacy. This necessitates a transition from the present patient-driven strategy to a more proactive one that prioritises data security and privacy across the EHR management process. By introducing more advanced permission models and robust security mechanisms, medical organisations can minimise the dangers associated with cloud-based EHR systems while also ensuring patient data privacy and security. Not only will this improve patient outcomes, but it will also shield healthcare organisations from legal and financial responsibilities.
Comparative Analysis of Detection of Text from Morse Code in Handwritten Images using Convolutional Neural Networks

One of the oldest techniques used in telecommunication for encoding regular characters is Morse Code. Morse Code is categorized into two separate electronic pulses which are dot (aka short pulse) and dash (aka long pulse). Detection of text from images of morse code is a complex process and there is no active research on this area As these are morse code images, different images have different styles of strokes. Our work aims to develop an Automated Morse code recognition system which is trained by a CNN (convolutional neural network) model with a self-built dataset and involves in collecting and preprocessing images of Morse code characters and creating a labeled dataset for training and testing the CNN model. The dataset creation process includes capturing images of different Morse code characters, augmenting the data to increase the dataset size, and annotating the images to label them correctly. The CNN model is then trained using the created dataset and evaluated for its accuracy in recognizing Morse code characters in images. The results demonstrate comparative analysis of different CNN based frameworks and achieved high accuracy in recognizing Morse code characters in images, making it a promising solution for automated Morse code recognition systems.
Trip Saathi: Travel Planning System based on Sentiment Analysis of Reviews

In India there are lot of places, some less explored and some places have been the epicentre of tourism. Many travel agencies and some enthusiastic people plan the journey with travel and stay. The tourists have a wide array of experiences ranging from positive, neutral to negative. Using the feedback from these people, many others plan their journey. This paper proposes a system to accumulate all feedback at one place and provide sentimental analysis. The result will help any person to have a well-informed decision.
AI based Smart Conferencing System using Gesture Recognition

The paper proposes the use of computer vision to translate sign language into text and audio, which can aid in communication for individuals with hearing loss. The system consists of four modules: picture capture, pre-processing, classification, and prediction. The segmentation of sign motions is achieved through image processing using Media Pipe in conjunction with the OpenCV Python package. To ensure high accuracy in predicting sign language, captured motions are scaled, converted to greyscale, and filtered for noise. The SVM classifier is utilized for classification and prediction. Additionally, natural language processing is employed to create meaningful sentences from the recognized sign gestures. Overall, this initiative has the potential to be a useful tool for those who have hearing loss and might greatly improve their capacity for interpersonal communication.
Image to Audio Conversion to Aid Visually Impaired People by CNN

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired.
Blood Cell Counting and Malaria Pathogen Detection using Convolutional Neural Network

The Whole Blood Cell Count is a crucial component of determining a human’s overall health and detecting different abnormalities such as anemia, infection, and leukemia. During this test, the quantity of different blood cells found in an individual is evaluated. Automating this approach would boost diagnosis effectiveness while lowering treatment costs in general. The objective of the paper is to utilize a convolutional neural network to carry out a comprehensive blood cell count through the examination of blood smear images. The model is additionally trained to identify the presence of malarial pathogens in the blood. According to the examinations conducted, the system performs with an average mean accuracy of over 0.95 when compared to the ground truth. Moreover, the model identifies the images that contain malarial parasites as infected with a 100% accuracy rate. Additionally, the software is adapted for use on an inexpensive microcomputer to facilitate fast prototyping.
Comparison and Analysis of Popular Frontend Frameworks and Libraries: An Evaluation of Parameters for Frontend Web Development

Creating a fantastic online experience for users relies heavily on strong and innovative front-end web development. A quick introduction to the fundamentals and significance of frontend web development is provided in this study. This research examines and compares widely used frontend frameworks and libraries based on a number of criteria. Inclusive important criteria are usability, performance, scalability, user engagement, and reliability. By contrasting and analysing the available frontend frameworks, developers are better able to weigh the pros and cons of each option and choose the best one for their web development project environment.
Development of Recommender Systems for Better Services and Products using Data Science

Online social networking and e-commerce are becoming increasingly popular. Recommender Systems (RS) let users find relevant information from several possibilities. Internet applications currently need RS. This technology uses huge data to provide customized suggestions to improve customer happiness. Concerns and ideas help customers choose items. Sentiment Analysis (SA) may increase RS recommendation accuracy by improving user behaviour, views, and responses. RS solves information overload in information retrieval, but data sparsity remains a big problem. SA is notable for reading text and expressing user preferences. It helps E-Commerce to monitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recommendation approach to increase RS accuracy and correctness. The hybrid approach beats standard models in several assessment criteria. Modern retailing businesses’ e-commerce operations are impossible without RSs. The content-based and context-aware techniques are hybridized for providing promising results. Content-based approaches connect consumers to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithms propose customized items with minimal use history. These systems use case-based recommendations or limitations to make recommendations. Finally, ensemble recommender systems combine data source prediction power.
An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

Face detection methods have usually been used mostly for faces that aren’t covered up (non-masked faces). These include nose, lips, ears, chin, and eyes. Faces must be concealed by masks in various locations, including labs, hospitals, crime scenes, and areas with high levels of pollution. The global spread of coronavirus known as COVID-19 has disastrous effects on community wellbeing. This study aims to apply a model too complicated data, namely, image and real-time video image tasks for personal face identification with and without a mask. The healthcare industry is in a hazardous state. Numerous preventative measures, including the use of masks, are being set in place to restrict the spread of this sickness; WHO (World Health organization) highly encourages the use of a mask. This extremely large number of COVID-19 samples provides valuable information regarding the pandemic’s progression, as well as a possible way to encircle it to prevent future transfers. To prevent the transmission of the virus, several preventative measures have been implemented, including the use of masks. using a face mask that prevents droplet transfer discharged into the atmosphere, on either hand, may help avoid the outbreak. As a solution, the purpose of this function will be to form an FMD (Face Mask Detection) model which can be used as part of an embedded imaging system. It is expected to enhance the detection rate and achieve an accuracy of more than 99%.