Reviewerly/exHarmony huggingface dataset:
Arabic Machine Translation (ArMT) based on LSTM with Attention Mechanism Architecture

As Arabic is considered a low-resource and a rich morphology language. As result, Arabic is considered one of the most challenging languages in Machine Translation (MT). While numerous translation research concentrated on Indo-European languages, much less was made in Arabic. Therefore, the quality of Arabic Machine Translation (ArMT) continues to require improvement. Neural Machine Translation (NMT) is now the state-of-the-art in MT approaches. In this paper, we propose a model for two-way translation between the Arabic and English languages. The proposed model based on NMT and use the Long Short-Term Memory (LSTM) encoder-decoder model with attention mechanism. In the basic encoder–decoder performance is linked to the size of the input sentence, such that as the latter increases, performance diminishes swiftly. Attention mechanisms (AMs) are used to overcome this issue. The proposed model by combining LSTM and attention mechanism is capable to improve accuracy result of translation. The experimental results show that this proposed model improves accuracy of translation and reduces the loss.
Detection and Classification of Cyberbullying in Social Media using Text Mining

This research work intends to classify the texts associated with bullying contents in social media, especially twitter by using the text mining process. A Multi-Modal Detection and classification of Cyberbullying media is developed in the study. This model integrates textual, and metadata to identify the cyberbullying media in case of social networks. The process involves two phases training and test the cyberbullying data, where natural language processing (NLP) is applied as the pre-processing tool and then particle swarm optimisation is used as feature selection process. Finally, the study applies decision tree classifier to classify the instances associated with cyberbullying and after classification, these features are combined with text instances to detect the performance of the proposed model. The simulation is conducted to test the detection rate of the classifier than the existing methods. The results show that the proposed method achieves higher rate of classification and detection accuracy than the existing methods.
Composite Lightweight Authenticated Encryption Based on LED Block Cipher and PHOTON Hash Function for IoT Devices

IoT devices are being used in different environments recently. They are mostly resource-constrained, and therefore, their data security is crucial. Several lightweight cryptographic primitives were proposed to overcome the limitations of the devices while maintaining moderate security levels. Such primitives provide either encryption or authentication. The encryption must be authenticated by a Message Authentication Code (MA C) or hash function for better overall security. Therefore, an architecture of integrated lightweight authenticated encryption (AE) based on LED block cipher and PHOTON hash function is presented. LED and PHOTON architectures were combined while exploiting area-performance trade-offs and utilizing the shared internal functions. The architecture is designed in Verilog HDL, synthesized in Altera Quartus II and simulated on Field Programmable Gate Array (FPGA) devices. The individual design of LED utilizes 357 logic elements (LE) and PHOTON utilizes 852 LE resulting in a total of 1209 LE. The logic utilization of the proposed shared architecture is 1046 LE. The results reveal that 13.5 % reduction in logic area is achieved compared to the independent implementations of LED and PHOTON.
Search Engine Optimization by using Machine Learning for Web Page Classification

This study presents an original way for classifying websites into three groups depending on how well their content adheres to search engine optimization (SEO) standards. It is based on expert knowledge and machine learning algorithms. Here the classifiers developed, then trained to separate a sample of unknown (a web page) into 1 of 3 categories which recognize critical traits that influence the level of page alteration. By hand, subject-matter experts manually label the data in the training set. The experimental findings demonstrate that machine learning may be used to anticipate how much a web page will be adjusted to the SEO recommendations. The proposed technique is valuable because it paves the way for agents in software and system expert that automatically identify web-pages or particular web pages area, that need to be changed to adhere to SEO best practices, perhaps obtain improved search engine ranks as a result. The findings of this study also advance the topic of determining the best positions for ranking variables that search engines use to rank webpages. Earlier research’ results indicated the page title, meta description, and H1 tag (header), and body text should all be considered when constructing a web page are supported by the experiments conducted for this study. The creation of a fresh data set of manually annotated web pages as a result of this study is another output that might be applied to future research.
Bias Protected Attributes Data Balancing using Map Reduce

In the discipline of Data Mining (DM), researchers occasionally disregard the necessity to maintain a balanced distribution on a dataset. This is problematic since maintaining a balanced distribution is essential. It is possible that the result of the process of categorizing things will be significantly influenced by this factor. Most classifiers are designed to function based on the fundamental premise that the distribution of the data is approximately balanced. Therefore, the efficacy of the classification technique has simply dropped, and it is required to address this issue to find a solution to the problem that has been presented. This research will construct a Fuzzy clustering using SMOT approach for the purpose of establishing labels for minority classes. The goal of this technique is to prevent the model from developing bias based on any of the protected attributes, such as gender, race, etc. This strategy makes advantage of parallel processing to provide a method for achieving a balance in the distribution of the dataset. Because of this, the classification findings will be enhanced in terms of the accuracy with which they categorize. The metrics of accuracy, sensitivity, and specificity are obtained from the various combinations of methods, and they are then compared to the various machine learning models that are currently in existence.
Making Software Based on Human-Driven Design Case Study: SQL for non-experts

There are barriers between general public people and professionals or computers. This barrier can also be seen in an educational situation. The purpose of this study is to break the barriers and reduce the learning cost, misunderstandings, and conflicts among people, computers, and knowledge in studying. This study attempted to find a way to provide information to the general public by using the software. In this paper, “general people” stands for students or people learning new skills and professional knowledge or people who have no knowledge of the field. As a case study, this study will create a user-friendly SQL system for non-experts. Approaches are mainly presented from the UI design of software and the usage of professional terms and words. The concept of Human-Driven Design(HDD) is also used. This paper researches the balance of interaction between humans and technology when using software. Based on HDD, this study also considers the sustainability of the database field and creates an opportunity to consider the reasons why the wall between experts and non-experts appears.
IoT Based Tele-Sleep Bruxism Diagnosis Tool

Bruxism is a clinical syndrome characterized by teeth grinding or clenching. If you suffer from bruxism, you may have unintentionally clenched or ground your teeth while you were awake (awake bruxism) or while you were asleep (sleep bruxism). The most accurate ways to identify sleep bruxism are polysomnography and nightly sleep studies at a sleep clinic. Polysomnography will include heart and muscle monitoring, as well as pulse and blood pressure data. As a result, in this study, the key signals for diagnosing sleep bruxism-such as pulse rate, ECG (electrocardiogram), and EMG (Electromyogram) are collected and stored in IOT through wireless monitoring. The hardware tools are used to test ECG and EMG signals. If the person has a sleep bruxism episode, the microcontroller will give an alert via buzzer and transfer the data via IoT to the cloud, which can be accessed by the doctor for tele-monitoring.
Evaluating Research Trends Using Key Term Occurrences and Multivariate Mann-Kendall Test

This paper offers a quantifiable means for assessing the trend in the interest of scientific community in a particular research theme. Representing the research theme as a set of key terms, we investigate its trend within the scientific literature, using a corpus of paper metadata belonging to flagship journals and conferences that are relevant for the encompassing domain. Our method employs a multivariate version of the Mann-Kendall test that is applied to a multivariate time series of key term occurrences in order to discover if the trend of the research theme is ascending, descending or if there is no monotonic trend. Finally, an illustrative case study, examining the effectiveness of the proposed method for three research themes from Electronic Design Automation field, is presented.
Next Word Prediction in Telugu using RNN Mechanism

Exchanging textual content by entering information and sending it to others has become one of the most popular ways of information exchange these days. In this advanced age, we spend huge amount of time sending and receiving emails, additional facts and data to others through social networking websites. To make the exchange of textual information faster and better, word prediction systems are introduced that can anticipate the most likely word to come next, so that people do not have to type the following word, but rather can select a word from the suggested ones. In this work, we built a model to predict the following word for Telugu. Predicting the next word in a sequence can reduce the count of number of letters typed made by the user. Next word prediction is an application of NLP, also known as language modelling. Many applications use these systems such as, Autocorrect and is primarily used in emails/messages. It is also used by MS Word, Google search that predicts the next word based on your search history. In this work, we studied NLP and applied LSTM, a deep learning technique to carry out the next word prediction process.
Machine Learning based Custom Named Entity Recognition

The significant increase of digital data in real world also results in various challenges in extracting the intended entities from the input text. Entities extraction process plays an important role in Natural Language Processing (NLP) applications such as named entity recognition, machine translation, text analytics, parts of speech tagging, sentiment analysis etc. In the real world, there are huge number of Named Entities, the recent named entity recognizer, SpaCy API version 3.0 can extract eighteen types of entities. In order to create and extract the user defined custom entities, this research work has proposed a Custom Named Entity Recognition (CNER) model. This model uses few textual training datasets by including user defined custom entities. By using CNER model, ten user intended custom named entities were trained and extracted from 27,439 English words present in eight different domains and Wikipedia articles and finally the state-of-the-art performance results are obtained.
Recent Trends in Deep Learning for Natural Language Processing and Scope for Asian Languages

Natural language processing (NLP) studies the techniques and procedures that allow a machine to converse using human language. Recent advances in artificial intelligence and communication technology have considerably enhanced natural language processing applications. Due to improvements in deep learning, virtually every aspect of artificial intelligence, including natural language processing, has made substantial progress. Deep learning methods use many layers of neurons to construct a neural network. Recurrent Neural Networks and their variants like long short term (LSTM), bidirectional LSTM, are some of the most popular deep learning techniques. This article reviews deep learning techniques employed in NLP- specific approaches in the last few years. We also studied the issues faced by researchers while trying to apply deep learning to Asian languages. We also highlight some critical deep learning research work carried out recently for Indian and other Asian languages. In addition, we discuss fundamental linguistic processing challenges and suggest future scopes for the topic.
Design of Reward Functions for RL-based High-Speed Autonomous Driving

We aim to design a reward function for autonomous driving by reinforcement learning for achieving high-speed driving while maintaining training stability for reaching the racetrack's goal. High-speed driving is aggressive, such as running on the road's edge as fast as possible at corners. Thus, creating reinforcement learning agents that drive at high speeds and can reach a goal is difficult in racing competition situations because of running off the road or collisions with other objects. In general, human drivers see the road ahead and make control decisions. Therefore, we design a reward function to consider the road ahead depending on the driving speed. Through experiments in a simulator, we compared our proposed reward function with others proposed in previous works in terms of driving speed and the training stability about reaching the goal. As a result of the experiment, our proposed reward function achieves an improvement of lap time by 0.71 seconds (3 %) with only a 4.4 % loss in stability in reaching a goal compared to the most stable reward function proposed in previous work.
Fake Image Detection Using An Ensemble of CNN Models Specialized For Individual Face Parts

With the rapid increase of deep learning technology, creating human face images with artificial intelligence (AI) is becoming easier. Those generated images are coming up to images that humans cannot distinguish from authentic ones. It is essential to realize an accurate method to detect such fake images to avoid abusing them. In this paper, we propose a fake image detection using an ensemble model of convolutional neural network (CNN) models that focus on deepfake detection of individual face parts. Our results show that a combination of deepfake detection based on different face parts is effective. This idea can be adopted on partially manipulated deepfake images/videos.
Performance Analysis of Machine Learning Classification Approaches for Monkey Pox Disease Prediction

After covid-19 pandemic, many countries have the possibility of getting affected by Monkey Pox Virus. Monkey pox has the same symptoms of smallpox, chicken pox, and measles virus. In this work, the computational models are construed to predict the presence or absence of monkey pox virus. Eight different Classification algorithms including Decision Tree (DT), Random Forest Classification (RF), Naïve Bayes (NB), K-Nearest Neighbor algorithms (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boosting algorithm (AB), Gradient Boosting (GB) algorithm are used for the Classification of Monkey Pox disease. Four evaluation measures are used in this work to compute the accuracy of classification. Four measures F-Score, Accuracy, Precision, and Recall are used to compare the eight different types of classification algorithms. Based on experimental analysis, it was observed that highest accuracy of 71% is achieved by Gradient Boosting algorithm when compared to other algorithms.
A Novel Approach for Classification of Online Product Reviews using various Machine Learning Techniques

Sentiment Analysis is extensively used in different sectors like product analysis for identifying the customer needs. There are also examples where sentiment analysis concept is in action such in health care, Government sector, stock analysis. Customers share their genuine feedback about the products in online shopping sites. Also, it will be easy for the users to see the product review as well as the ratings given and then make a decision to buy the products. The purpose of sentiment classification is to analyze the written reviews of users and classify them into positive or negative opinions. It helps in identifying the issues with the product which in turn can be rectified. On the other hand, product reviews help customers to buy the product based on its review which would also help business owners to improvise. Text present in the product review is difficult to categorize sometimes. This can be achieved by using classification algorithms like Decision Tree, Random Forest, Naïve Bayes and Logistic Regression. There are multiple features available from which N-Gram and TF-IDF (Term Frequency-Inverse Document frequency) were used. With the results, it is evident that Random Forest performs better in TF-IDF and N-Gram approach for Electronics, Health and beauty and Clothing and accessories product type.
Comparative Analysis on Artificial Intelligence Technologies and its Application in FinTech

Universal technology has advanced with time, and as they say, “Necessity is the Mother of Invention,” this is impacting people's attitudes. As a result, people's attitudes regarding online networks have shifted from those of cable networks, which has steadily influenced the world's adoption of new technologies. Computerized Intelligence (AI). AI will enable financial businesses to totally rethink their business models, launch cutting-edge goods and services, and, most crucially, influence customer experience initiatives. In this second machine age, banks will face competition from nimble fintech startups that use cutting-edge technologies to supplement or even replace human labor with complex algorithms. Banking businesses will need to accept AI and use it into their business plan in order to keep a strong competitive advantage. As human selection is expensive, AI and ML are replacing human analysts in corporate tasks. Since AI is built on ML, it learns over time and provides the utmost accuracy in calculations and data analysis. AI also has the ability to introduce process automation to many industries as needed, as well as smart analytics and clear thought. AI-powered Chat-Bots have established themselves as a viable customer service tool, and because they save businesses time and money, they appear to be an uncomposable resource. The investments are a major part of the finance sector and taking in account of the investments prediction by the artificial intelligence algorithms and a comparison between them is the main motive behind this paper.
HTML+CSS Implementation based on Image Intelligent Scene Recognition Algorithm

HTML+CSS implementation of the productive training smart online platform based on garden image intelligent scene recognition algorithm is studied in the paper. Mathematical morphology mainly studies the mathematical theory in space. Its mathematical description language is set theory. The most basic operations include expansion and corrosion, opening and closing. Through the general combined use of the above methods, the morphological structure of the image is obtained. Based on this theoretical background, a novel image intelligent scene recognition algorithm has been proposed. The level and its frequency are taken together as the data samples to be then clustered, and a fast FCM image clustering algorithm based on histogram weighting is constructed. Furthermore, to then implement the system, the HTML+CSS is considered. In order to solve the cross-terminal and adaptability of the web page layout, HTML5 technology provides a meta element called viewport and the novel CSS tool gives the better definition. Through the experimental analysis, the performance is then validated.
A Survey on Alleviating the Naive Bayes Conditional Independence Assumption

Naive Bayes (NB) is a simple and effective probabilistic classification method based on the Bayes theorem. NB method is used in a wide variety of real-world applications due to its effectiveness and ease of implementation. These applications include product recommendations, medical domain, credit scoring, sentiment analysis, spam filtering, etc. One of the keys to NB's efficacy is a stringent stipulation that the dataset's attributes are all treated as equal and conditionally independent. But in real-time, the NB assumption about the dataset cannot be satisfied, since the dataset captured has a high possibility of holding correlated, irrelevant, and uncertain variables. The use of these datasets does not satisfy the NB assumption, and results in poor prediction of the model. Many researchers have come up with different ways to solve the problem of the conditional independence presumption. The proposed method can be broken down into four main parts: Local Learning, Data Expansion, Feature Selection and Weighting, and Structure Extension. The goal of this study is to carry out an in-depth survey on the various methodologies that have been suggested as a means of mitigating the conditional independence presumption and determining how the performance of NB can be improved.
Detection of Indian Fake Currency using Image Processing

The fake currency notes are detected using image processing employing MATLAB in this paper. This project aims in providing the best techniques in image acquisition, and image segmentation. The work uses CANNY's algorithm to extract the notes' features much more effectively. Algorithms for image processing are used to extract the options. The technique used here functions well with the recently introduced denominations of 500 and 2000. The recommended strategy provides a practical means of detecting fake currency that is supported by physical inspection.
Object Identification from Dark/Blurred Image using WBWM and Gaussian Pyramid Techniques

Dehazing algorithm development has recently attracted many researchers. The dehazing algorithms are used in a variety of multimedia related research fields for image restoration, enhancement and segmentation. These researches have assisted us in correcting certain accidentally blurred photographs that were caused by motion blur distortion, out-of-focus objects, intense light, and physical flaws in camera lenses. In this paper, we aims to show the different dehazing techniques for the removal of haze in the images. This paper also discuss about the proposed prominent algorithm called White Balance Weight Map (WBWM) for dehazing. This algorithm is suitable for haze removal from images in both dark and light view. The white balance is used for enhancing the image contrast and the weight map is used for restoration of pixels for better view in hazed image. By using this algorithm we can visualize the objects clearly that present in the dehazed images. The main goal of this work is to give readers an intuitive grasp of the dehazing technique and white balance and weight map algorithm which have made tremendous progress in haze reduction. The effectiveness of the proposed algorithm is substantiated with the help of the image quality parameters calculated. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method.