Reviewerly/exHarmony huggingface dataset:
Domain-Specific Text Generation for Arabic Text Summarization

As more and more long-form content in the domain of public health is being published on the Internet, it is becoming increasingly time-consuming for someone like advice seeker, healthcare professionals, and researchers to filter out the content they want. Proper summaries can help these people to quickly go through the highlights of a huge amount of textual content and save an important amount of time to study the relevant documents. The emergence of large language models has redefined the state of the arts for automatic text summarization. However, the availability of in-domain datasets for the task of text summarization is scarce, and such datasets are hard to construct. In this work, we build an abstract text summarizer model for the Arabic language content using the state-of-the-art “Transformer” language model. We propose an iterative data augmentation technique which uses synthetic in-domain data along with the real summarization in-domain data for Arabic. We investigate the performance of our model based on a fine-tuned BART model using a test set. We show that this strategy produces better summarization performance in terms of ROUGE score. We achieve a slight improvement of +0.6 and +0.5 in ROUGE1 F1 (R1 F1) on the development and test sets, respectively. We compare it to the model which does not rely on data augmentation.
The Evolution of Cryptocurrency and Cyber Attacks

Cryptocurrencies characteristics facilitated ran-somware attacks extorting payments in the form of Bitcoin, Mon-ero, Ethereum and others from individuals and organizations. The open cyber boarders allowed anyone to own cryptocurrency without revealing their identities. This research investigates the role of cryptocurrency in the increase of cyber attacks and creating the new phenomenon of crypto-ransomware and answer two fundamental questions: (1) Is there a correlation between the increase use of cryptocurrency and the spread of cyber attacks? And (2) Would ransomware attacks exist if cryptocurrency did not exist?.
Application of Fuzzy Matching Algorithms for Doctors Handwriting Recognition

Doctor's handwritten prescriptions are often known to be indecipherable. Uncertainty in medical terms can have dire consequences. A method to effectively recognize medicine names written in doctor's handwriting is proposed in this paper. A corpus of 600 images is compiled with the help of multiple doctors. An exhaustive list of 50 medicines is used for the same. Recognition is performed using the Convolutional Recurrent Neural Network (CRNN) - Connectionist Temporal Classification (CTC) model which results in 93.3 % accuracy. In order to deal with errors produced in the recognized text, edit distance methods are further implemented and analyzed. Damerau-Levenshtein distance method is deemed to be the most suitable, yielding a well-grounded system for medicine name recognition.
Deep Learning for Crowd Image Classification for Images Captured Under Varying Climatic and Lighting Condition

Most of recent events have attracted a lot of attention towards importance of automatic crowd classification and management. COVID-19 is the most setback for the entire world. During these events proper breakout and public crowd management leads to the requirement of managing, counting, securing as well as tracking the crowd. But automatic analysis of the crowd is very challenging task because of varying climatic and lighting conditions, varying postures etc. During this paper we have developed PYTHON based system for automatic crowd images classification using Deep learning. This paper is the first attempt for automatic classification of crowd images. We have prepared the dataset of crowd classification consisting of three categories. The proposed methodology of crowd classification starts with preprocessing during which we have used median filtering for noise removal. Deep learning models are developed using 70% training images. The performance of the system is evaluated for various deep learning algorithms including one block VGG, two block VGG and three block VGG. We have also evaluated the performance of three block VGG using dropout. VGG16 transfer learning based crowd classification is developed using PYTHON. Using VGG16 transfer learning we achieved the accuracy of 69.44.% which is highest among all deep learning classification models during this study
Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

The increasing workload and the number of academic information needs in the campus environment make the academic part of the stress. The addition of students and plans for additional study programs that will continue to be carried out by universities make academic staff tired and slow to work because they cannot provide fast and appropriate services to students and the academic community. Artificial Intelligence (AI) based chatbot technology is present to help perform specific tasks similar to academic officers. With the KNearest Neighbor (K-NN) method, the highest K value in the third class is 55.70 percent. This value explains that problems related to text classification can be solved on K-NN and give good results. In this evaluation, customer response times were significantly shorter when using chatbot technology compared to response times before using them. In addition, the work done by academic staff began to decrease, while the accuracy of chatbots remained at 100 percent in tests that compared chatbots with academic staff. Therefore, the evaluation results show that chatbots effectively increase efficiency in handling customer inquiries. There were 62 respondents consisting of 13.8 percent of lecturers, 9.2 percent of staff, and 76.9 percent of students who implemented chatbots. Testing of chatbot technology includes ability, consistency, responsibility, and performance. The validity test uses a significance level of 5 percent. The test results found that the level of influence of the use of chatbot technology by users in obtaining academic information was more reliable, with the acquisition of the Cronbach Alpha value of 0.82. A solution offered for the academic community and the academic community to access services more quickly and practically by using a chatbot. Chatbots can also reduce the workload of academic staff and affect the quality of service in universities to be more optimal.
A Study of LIME and SHAP Model Explainers for Autonomous Disease Predictions

Autonomous disease prediction systems are the new normal in the health industry today. These systems are used for decision support for medical practitioners and work based on users' health details input. These systems are based on Machine Learning models for generating predictions but at the same time are not capable to explain the rationale behind their prediction as the data size grows exponentially, resulting in the lack of user trust and transparency in the decision-making abilities of these systems. Explainable AI (XAI) can help users understand and interpret such autonomous predictions helping to restore the users' trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for their applicability in autonomous disease prediction.
Simple Fuzzy Decision Support Model for Evaluating the Cryptocurrency’s Performance

Evaluation of cryptocurrency’s performance is performed questionably. There is no role for computer-model, make such an evaluation process does not have guidance. In this study, a simple decision support model (DSM) based on fuzzy logic was academically constructed to observe the cryptocurrency’s performance. By operating the primary method of fuzzy logic and taking into account three types of parameters (i.e. time-series close price data, daily max-min price, and transaction number), a novel DSM for evaluating the cryptocurrency’s performance was fruitfully executed. Based on three types of real cryptocurrency six-month data (i.e. Bitcoin Ethereum, and Dogecoin), the model could irreversibly expose that Bitcoin has the best performance with 36.39 performance points.
Augmented Reality in Education: An Interactive way to Learn

In recent times the world has gone through a situation where the people were forced to be in a lockdown amidst the covid-19 pandemic. Various business fields were affected by this phenomenon, some business units have evolved through this staying at home and carrying the work culture. One of the most affected fields was education where the children were made to attend online classes and read from E-textbooks. People are being educated from various methods including offline classroom lectures, physical textbooks etc. The choice of learning methodology is dependent on an individual's access to various technologies, environment, and a person's surroundings. Augmented Reality (AR) is a technology that enables the superimposing of artificially made 3D models on top of reality. This paper describes Augmented Reality (AR), how it can be used for interactive education, and help children visualize the concepts for gaining an improved understanding over the educational concepts.
Procurement System Using Blockchain

This paper is about an integrated platform based on blockchain technology to serve the purpose of a procurement system that involves the following stakeholders-vendor Industry/Customer Delivery executive. This is a web application- based solution for a modern and secure procurement system that could replace the conventional systems. It involves various blockchain frameworks and works on smart contracts. A public framework named “Ethereum” is used to implement the functioning of this procurement system. Transactions are made based on smart contracts via wallets. Blockchain is one of the highly reliable technologies that ensures security authenticity integrity and reliability. Since all the processes during the procurement is taken care by blockchain there is no need for a third party or a payment gateway. This proves to be advantageous. This solution involves cutting edge technology like Blockchain and frameworks like “Ethereum” “Metamask” “Truffle” “Ganache” to make the current system smarter and effective.
Prediction and Prevention Analysis Using Machine Learning Algorithms for Detecting the Crime Data

Crime is a fragment of substantial and predominating concerns in our society and its preclusion is key task Day to day there are huge numbers of offense committed sequentially. This requisite retain track of all the offense and preserving a database for same which may be used for succeeding reference. The present problem faced are maintaining of appropriate dataset of crime and evaluating this data to help in predicting and solving crimes in future. The objective of this project is to dataset which includes several crimes and forecasting the crime types which may transpire in future determining upon various constraints. In this intend, we will be utilizing the technique of machine learning and data science. Prior implementing of the replica model data pre-processing will be done ensuring this feature preference and grading will be done so that precision obtained will be excessive. The K-Nearest Neighbour (KNN) categorization and various other algorithms will be examined for crime data prediction and prevention one with progressing accuracy will be used for implementing. Delusion of dataset will be accepted in terms of graphical depiction of many occurrence for case in point at which duration the culprit rates are huge or at which month the culprit activities are peak. The essence purpose of this intend is to provide a jest idea of how machine learning can be used by the law enforcement agencies to detect, predict and prevent solving crimes at a much quicker rate and thus decrease the crime data.
ModifiedECS (mECS) Algorithm for Madurese-Indonesian Rule-Based Machine Translation

The Madurese language is a local Indonesian culture that needs to be preserved. The morphology of Madurese words is unique, and there are several forms of words: affixation, root word, degree modifier, and reduplication word. Each word has a pattern that Rule-Based Machine Translation (RBMT) uses to ensure accurate translation. RBMT requires a stemming process to convert each word into its root word. The Madurese language’s morphology has several unique characteristics that need careful study, including the affix. An affix attached to a word can have different meanings depending on the conditions. This paper develops a new stemming algorithm called modified ECS (mECS), i.e., a modification of ECS stemming combined with the concept of Rule-Based word morphology. The system was tested using 50 Madurese language sentences randomly selected from a Madurese language textbook for the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -grade elementary school Madura language learning textbook. The accuracy of proposed RBMT system that implements the newly developed mECS stemming algorithm is 85%.
Plane Detection and Product Trail using Augmented Reality

Information and communication expertise (ICT) nowadays ropes the growth of human contact with corporeal, digital, and practical environments including research, business, banking, and education, among others. The blending of reality and digital information is the focus of the computer science field known as augmented reality (AR). In the beginning, consumers could purchase furniture items without going to a store, but they couldn't see how they would look in their actual homes. Now, a user of our proposed system can purchase furniture items while seated at home rather than going to a store. The main determination of the “Furniture Layout Application Using Augmented Reality” is to create an Android application that allows users of mobile devices with AR cameras to virtually check out various furnishings. The time-consuming task of physically visiting a furniture store will no longer require human effort thanks to the programme. In addition, it might be simpler to apply this strategy when shopping online because it gives customers the chance to test out the pieces of furniture they are considering buying in their rooms and see how they will fit in there. Without physically moving any furniture pieces, a user can theoretically experiment with many different combinations. By developing a furniture AR application, the aim is to advance accessibility and time competence for furniture try-on. Before purchasing the item, the consumer can use this method to digitally view the furniture item in a genuine environment. The consumer will learn how his home construction will look after acquiring the furniture item thanks to this approach. This method would enable the operator to numerically test out various object groupings without actually moving any furniture pieces. These will aid the purchaser in deciding how to assemble furniture within the home's framework.
Security and Privacy-Efficient Encryption Algorithm for Cloud Data Using Genetic Prime Crossover Technique

Cloud Storage has become one of the essential services over the last decade. Simultaneously, the risk of storing data in a cloud environment is also increasing. Cloud security primarily focuses on data privacy and security aspects of Cloud Storage. Although security issues are taken care by trending cloud providers AWS, Microsoft Azure and Google cloud to some extent. Securing the cloud data from intruders and malicious attackers is a growing phenomenon at present times. In this research work, using genetic crossover technique a novel encryption algorithm is introduced for storing sensitive and non-sensitive data on a heterogeneous multi-cloud environment to prevent most vulnerable activities like data breaches, man-in-the-middle attacks, and insider attacks. The proposed prime crossover technique is used to encrypt the file and store it in multiple cloud-environments to enhance security of data. In such a way integrity, confidentiality and availability of data are not compromised. Experimental results of the proposed novel prime crossover technique are shown.
Human Action Recognition Using Deep Learning Methods (CNN-LSTM) Without Sensors

Surveillance System is gaining attention in recent years in the computer vision field that's where the Human Activity Recognition using videos feed plays an important role. The Human Activity Recognition is a Challenging task to implement in real life technology because of the high computation and accuracy involved. This paper proposes a method for Recognising Activities from video input feed. In this paper we are using Long Short Term Memory (.LSTM) and Convolutional Neural Network (C.N.N) to classify the videos and train the model. This model is trained using UCF-50 Dataset, it consists of 50 activities. The HAR system is widely used in many applications such as Surveillance, healthcare, security fields etc. The main objective of this paper is to recognize activities performed by the humans using videos dataset as an input and using machine and deep learning libraries.
Fake Logo Detection

The objective of this study is to recognize a fake product logo and assess how much it resembles a legitimate product logo. We used the YOLO technique and a Darknet framework to construct a fake logo detector.. For a dataset, the logo of copyrighted products is being used as a sample. Most of this research will be influenced by the YOLO algorithm. Brand visibility monitoring, copyright violation detection, and social media product brand management are all examples of uses for logo detection. With the high level of accuracy, we are able to identify the Fake logo.
Social Distance Alerting System Using Machine Learning

The increase into the Corona virus pandemic led to a higher death rate globally. The best way to prevent getting sick is to keep yourself physically or socially far. Our project provides an approach for physical isolation revealing using machine knowledge toward indicate the necessary space to be maintained to decrease the collision of the corona virus contagious widespread spread. By analyzing a videotape provide for from the camera, the detect apparatus be fashioned in the direction of notify individuals toward maintain a out of harm's way aloofness on or after one an additional. The open-source person recognition pretrained model, YOLO3 algorithm, was utilized to recognize people using the video frame from the camera as input. YOLO3 has the benefit of mortal a lot quicker than further algorithms, at a halt maintain exactness and meets the real-time requirements for person detection. In order to calculate distance from the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2D}$</tex> plane, the video frames are afterwards transformed into top-down views. Estimated distance between individuals and any non-compliant pair of individuals within the display is indicate by means of a red colour edge and stripe, the moderate distance is represented with orange colour and the safe distance is represented by green colour frame. The suggested technique was examined lying on a pre record videotape as well as on the live video feed of persons walking on the road. Additionally an alarm sound is provided to notify the persons. The outcome show that the planned strategy is ready toward sees the societal separation trial among many populaces withinthe videotape.
A Deep Learning Approach for Twitter Bot Detection

Twitter, a social networking platform allows users to convey their ideas. on a wide range of topics, including politics, sports, the stock market, and entertainment. It has a big influence on how people think. A bot on Twitter sends spam messages. As a result, detecting bots aids in spam detection. In this paper, the detection of twitter bots using Deep Learning methods is addressed. At present, the used models aren't updated with latest datasets and have reduced accuracy and some aren't multilingual. A final classifier, Bot-DenseNet, is built on a dense neural network on combining additional metadata with text encodings. Existing methods consider metadata information or along with some semantic features of text in encoding the user account. It will be trained and then verified using extensive data sets collected from Kaggle and twitter API. Subsequently, comparison between the performance of the Bot-DenseNet and Bag-of-Words model is also analyzed.
Identifying spammer groups in consumer reviews using meta-data via bipartite graph approach

Nowadays, online product reviews are more common on e-commerce platforms. Before making a purchase, people frequently consult product reviews to assess the quality of the item. However, the review system has been seriously harmed by a huge number of review spammers, who frequently cooperate to promote or denigrate specific products. Earlier research uses machine learning techniques to identify singleton suspicious reviews and reviewers without considering the meta-data. In this study, we utilise the meta-data of the consumer’s reviews to identify review spammer organisations using the state-of the-art community detection techniques. Due to the diversity of behavioural indicators, group spammers are challenging to identify. In this study, we propose that clustering the singleton spammers using the meta-data (location and time) of the reviews is the key to identifying group spammers (and their fraudulent reviews). We propose filling out the review-product matrix using the product and review information and text. We then use this to deduce the hidden reviewer-product connections to address the issue of the absence of explicit behavioural signals for singleton reviewers. Subsequently, we build a bipartite graph using the review-product matrix. Using the meta-data of the reviews, which are frequently overlooked by existing algorithms, experiments on a real-world Yelp dataset demonstrated the effectiveness of our methodology in detecting group spammers.
CNN -LSTM Based Approach for Recognition of Devanagari Manuscripts

Devanagari scripts are a good source of information as it consists medieval and prestigious content which is very useful for the upcoming generations. Digitization of these scripts becomes one of the most important task to make the content available. Pattern recognition emerges its one of the applications as Optical character recognition. For the character recognition from manuscripts, the authors have used CNN which gives higher character recognition accuracy. This paper implements Convolution Neural Network + Long Short Term Memory for Devanagari character recognition. The total of 250 manuscript pages were considered and the dataset of multiple characters were divided into 33 classes of basic characters. The proposed model was run using 15:85 and 25:75 as test:train ratio of characters. Also, the number of epochs were varied for better recognition accuracy. The authors observed the best recognition accuracy of 93.63% when the proposed model was run with 25:75 test:train data on 27 epochs.