Reviewerly/exHarmony huggingface dataset:
Study on Book Recommendation System

A recommendation system aims to suggest certain item or product to specific users based on the user's preference, interest, and rating. It is essential to create associations between objects to deliver the best RS. Today, majority of e-commerce businesses use recommendation algorithms to entice customers to make additional purchases by presenting things that they are likely to like. RS facilitate rapid navigation and information gathering. Reading offers benefits for both individuals and societies as a whole, studies suggest a reduction in reading among young people in particular. The book recommendation systems help readers select the right book for them. The BRS is used by retailers to manage their inventory and boost profits. RS will make it easier to stop this decrease. BRS assists librarians in effectively managing the library catalogue. In this paper a survey on varrious methods applied to BRS is presented. The advantages of adopting a technique, additional techniques that have been used to enhance the BRS and BRS applications are also reviewed. Amazon, Barnes & Noble, Flip cart, Goodread, and other online retailers employ BRS to suggest books that customers might be enticed to purchase because they fit their preferences.
Online Phishing Detection Using Machine Learning

Online phishing detection has gained significant importance, which will only grow with the amount of dependency on cyberspace, the proposed system provides an easy solution, during cases when the user is unsure about the authenticity of the website visited, they can try to copy the Uniform Resource Locator (URL) and paste the link into the online phishing detection system. Through the system process, it will help the user to identify whether given links were legitimate website or it is a phishing website. Therefore, the user will not be in a doubtful situation the whole day in wondering whether the information they gave in a certain website is safe or not. Providing complex decision with simplicity, the system will help the user to detect each variable of URL given accurately. Machine learning will not only able to detect each of the variables, but the system will also learn to determine the element of phishing inside of the URL. Two classifiers are used in order to detect the element of phishing which are Random Forest classifiers and Support Vector Machine (SVM).
Blockchain-based System for Secure Storage and Sharing of Diabetics Healthcare Records

The number of diabetic patients in Saudi Arabia has been increasing recently. Since those patients often visit various healthcare institutions, large amounts of data related to their general health are generated. This imposes a challenge on how to securely store this data and make it accessible for authorized healthcare institutions treating the patient. In this paper, we propose a system that combines blockchain technology with cryptographic primitives and decentralized storage systems to securely store and share medical records for diabetic patients. We analyze the security of the proposed scheme in terms of data confidentiality, data integrity, data availability, and user privacy. We encipher the healthcare records and store them in the distributed InterPlanetary File System (IPFS). This is to preserve the secrecy of sensitive healthcare data and to avoid the single point of failure issues. Blockchain is used only to store the hash value of the healthcare records and their location in the IPFS, resulting in lower operational costs while preserving the integrity of the healthcare data. To enforce fine-grained access control, our system allows the patient to determine who can access their medical records with the help of an attributebased encryption scheme. Only authorized users can decipher and access the original health data if they own the appropriate attributes defined in the access policy.
Blood cells image segmentation and counting using deep transfer learning

In this paper, we present a two-step automatic blood cell counting approach for accurately and efficiently determining the complete blood count (CBC). The approach involves using two convolutional neural networks (CNNs) for the segmentation of red blood cells, white blood cells, and platelets, and then applying three different algorithms (Watershed, Connected Component Labeling, and Circle Hough Transform) to count the cells present in the masks produced by the CNNs. We also introduce a loss function for the Circle Hough Transform algorithm to further improve its accuracy. Our approach shows good results compared to other methods in the literature and has the potential to significantly reduce the time and effort required for manual blood cell counting.
Document Provenance and Authentication through Authorship Classification

Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms including classical Machine Learning (ML) algorithms, transformers, and deep learning algorithms both individually and in merit-based late fusion. For the merit-based late fusion, we employed several weight optimization and selection methods to assign merit-based weights to the individual text classification algorithms. We also analyze the impact of the characters on the task that are usually excluded in NLP applications during pre-processing by conducting experiments on both clean and un-clean data. The proposed framework is evaluated on a large-scale benchmark dataset, significantly improving performance over the existing solutions.
Place Recognition Systems by Old Photos using Machine Learning Algorithm

In recent years, the gigantic assortments of imagery on the Internet have encouraged a rush of approaches to location recognition, the trouble of figuring out where a photograph was taken by contrasting it to a database comprising images of recently seen areas. Because of the immense number of images constructing a world-scale location recognition engine from the entirety of the geo-labeled images from online photograph collections, such as Flicker and road-seen databases from Microsoft and Google, excitement in this area is increasing. Matching historical images to modern places requires extra effort from dealing with historical photos. Many matching techniques work effectively on modern images but not historical ones. Here in this paper, a survey of different place recognition methods is given to get a clear idea for developing an efficient system for recognizing old photos given modern labeled images collected from the Internet.
Flash Flood Simulation for Assisting Children to Understand the Flood Disaster

This project aims to educate children on situation of flash flood. A development of a 3D simulation of flash flood and problem solver in the situation are created. With such simulation it can assist children to visualize, understand the danger and take right action. This simulation project is designed specifically for a place rarely affected with flood such as Shah Alam, Selangor. The main target audience for this project is children age between 9 to 12 years old. The application tested on 68 children and based on the result, it can be concluded that they learn and understand the flood issue quicker with the simulation.
DARIJA-C: towards a Moroccan DARIJA Speech recognition and speech-to-text Translation Corpus

This paper introduces an automated collection of a speech corpus for the Moroccan Arabic dialect “Darija” (DARIJA-C) which is intended for speech-to-text translation from Moroccan Darija into classical Arabic language. The DARIJA-C corpus is designed for Moroccan Darija automatic speech-to-text translation purposes. Nevertheless, it can be useful for automatic speech recognition of this dialect. To address both scale and sustainability, the DARIJA-C project uses crowdsourcing to collect and validate speech transcriptions and translations. By providing an automatic web platform for recording speech, along with their corresponding translation by distinct unknown speakers. The first versions of the Darija-C dataset will include only the translation of Moroccan Darija speech to classical Arabic. In later versions, we will include the translation of Moroccan Darija into other international languages such as French, and English, … The goal of this work is to build the largest crowdsourced corpus of Darija speech, which to our knowledge will be the first corpus for Moroccan Darija Speech-to-text translation.
Detection of Credit Card Fraud Using Resampling and Boosting Technique

There are more online card transactions as a result of the development of technologies like financial technology and e-commerce applications. Fraud on credit cards has skyrock-eted, as a result affecting credit card companies, customers, retailers, and banks. Therefore, it is crucial to create systems that guarantee the confidentiality and accuracy of credit card transactions. Using Sparkov's imbalanced synthetic dataset, a Machine Learning (ML)-based remedy for fraud detection using credit cards is developed. Use of a synthetic dataset with named attributes distinguishes this proposed approach from the usual implementation of ML models using publicly available European Credit Card Fraud(CCF) Dataset in Kaggle. Most of the studies in this field are done with this dataset as a basis. To tackle the problem of class imbalance, the Synthetic Minority Oversampling Technique(SMOTE) named approach is used. The framework is then evaluated using Extra Tree (ET), Gradient Boost (GB), Decision Tree(DT) and Random Forest (RF). The above ML models were combined with the ensemble model known as AdaBoost to improve the quality of automated detection. Accuracy, Recall, Precision and F1-score are used to assess the models. To further validate the findings of this study, the suggested framework was applied to a synthetic dataset of credit card transactions which are highly skewed. The ML algorithms are paired with the AdaBoost algorithm to check its impact on the efficiency of the proposed method.
Network Traffic Classification Using Supervised Learning Algorithms

Network traffic classification is crucial for traffic monitoring and application-based policy enforcement. However, the widespread use of encrypted protocols has greatly challenged conventional traffic classification techniques using packet payload and port numbers. For the network application in this paper, two machine learning algorithms, Decision Tree (DT) and Random Forest (RF) are used. An open-access Kaggle dataset with six different types of applications is used for this study. To achieve the best values for model training, loop iteration is used rather than the hyper-parameter optimization technique. When compared to DT, RF has the highest accuracy (99.72%). In order to improve the classification process and various hidden patterns connected with the statistical features, more statistical features were taken into account in comparison to other related works that had already been done. The outcomes demonstrate the potency of supervised learning algorithms for categorizing network traffic.
Model of Visualization and Analytics for Open Data (Case: Election Voters &amp; Kids Disability Category)

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.
Application of Advanced Deep Learning Techniques for Face Detection and Age Estimation

In recent years, AI-based algorithms have been used to solve a wide range of previously intractable issues. Face detection and recognition from large groups, as well as feature identification from a description, are two of the most important security issues that can be addressed with image processing and modern AI. In this research, we use deep learning to analyze facial images to detect faces and estimate ages. The proposed method was used on photos of King Abdullah bin Abdulaziz AL Saud to determine his face and approximate his age. For this purpose, we relied on powerful deep learning models like DELWO and MTCNN to perform tasks like identifying faces and determining how old they are. The findings are very encouraging and promising. The experimental results have shown that the proposed method has good accuracy for face recognition (98.50%) and age estimation (82%).
Semantic Image Style Transfer

An image is a pictorial illustration of some object. The value of the image can be generated using a technique named as image style transfer method. Semantic content of a picture is an important aspect in the image processing also it is one of the complex methodologies in the image processing which helps the users to transfer an image into another format or another image. Previous approach has lack of image representation which explicitly characterize the semantic information which process the content of the image from different styles. It was advised to use the representation of image using the CNN (convolutional Neural Networks) which helps to recognize the object and gives high level image information. Creating style to splits and combines the style and context of the image which allows high perceptual quality image which combines the appearance of a snap into artwork. This approach generates a new visualization to illustrate the deep image which potentially provides a high and synthesis of image using the global by combining there different global content loss, global feature loss and global style loss.
Single Target Visual Object Tracking

In the present world, the evolution of technology takes place in terms of developing new things to solve some real-world problems. Almost all fields are creative in the sense of creating new things. Computer vision is one of the fields that give the technology that can help with different real-time problems. This field offers some useful applications over the last few decades. Different technologies like Autonomous cars, Traffic flow analysis, X-ray analysis, etc. make this field interesting for researchers. Object detection is one of the applications of Computer vision. Object detection and tracking enable the machine to detect the object and track the object through the entire scene. In this paper, a supervised learning approach for object detection is presented which consists of a detector, a Neural network, Images, and Groundtruths. The presented model successfully tracks the object and the dataset used for the performance evaluation is Online Object Tracking Benchmark (OOTB), Unmanned Aerial Vehicle (UAV), and Visual Object Tracking Benchmark (VOT) dataset.
Twitter Sentimental Analysis using Machine Learning Approaches for SemeVal Dataset

In recent years, Sentimental Analysis has enticed many researchers in this field. Due to the lack of suitable datasets, many scientists and researchers faced hindrances in their research. We used Semeval dataset because it’s the authentic dataset for computational sentimental Analysis. When it comes to natural catastrophes, political turmoil, and terrorism, social scientists and psychologists are interested in learning how individuals express their feelings and opinions. In this paper, we present the approach to the SemEva1-2017 dataset. As we know, with the advancement of technology, social media have established strong worldwide connectivity and information sharing. The wide use of social media and media-networking sites produced an unprecedented amount of data. Sharing information using these websites has become very common. To detect the triggering factors has become necessary to understand the behavioural and emotional state to avoid anti-social behaviour and extreme or impulsive responses. We reveal to identify the emotional textual data using different strategies. Classification of the Tweets according to the Sentimental Analysis has an important role in the social, economic, and political world s. The effective strategy for tackling and coping with it is to use computational techniques to identify the speech type. For feature extraction, we use a variety of machine learning classifiers. It’s crucial to detect related features in a text correctly. As a result, using and improving NLP approaches can aid in improved understanding and analysis of data. Briefly, we use an unsupervised. TF-IDF for the Feature Extraction to train the word Embedding Techniques that are tuned into transform training data and transformed Test data. The model is finally initialized using vectorization on Twitter sentiment analysis to train the latter. Then, transformed the model to create the transformed dataset. The major findings and outcomes of SemEva1-2017 on Identifying and Categorizing the Sentiments of the Language in social media of Twitter are presented and evaluated results based on applying different classifiers for Machine learning Modeling.
Ensemble Learning And its Application in Spam Detection

An individual model is not always sufficient enough to classify an email. Each spam mail has features that distinguish it from any other regular mail. A model might not always use that feature for classification and thus produce erroneous results. It is essential to cross-verify the output of one model, with that of another model. This can be done using the ensemble learning technique. Previously, this was done using the same model repeatedly, or different variants of the model. However, in this paper, we have used four completely different models and used them to perform max voting, to optimize the result. The models used are Support Vector Machine(SVM), Multinomial Naïve Bayes(MNB), Random Forest(RF), and Decision Tree(DT). After testing all the possible combinations, we were able to conclude that the combination of SVM, MNB, and DT gives the optimal result.
Hybrid Sentiment Analysis Model with Majority Voting for Un-labeled Arabic Text

A large scale of data is posted every day on the social media platforms. Sentiment analysis classification is one of the useful techniques to extract useful information from those data. To train sentiment analysis models there is a need for labeled data, and it is one of the challenging issues. The available datasets are collected and labeled manually by humans which is a time-consuming process. The identification of machine learning classifier that provides the best performance is a second issue in sentiment analysis. A new hybrid sentiment analysis model is proposed in this paper. It relies on the use of the lexicon-based approach and majority voting. The best performance of the proposed model is when using the set of classifiers: NB, LogReg and SGD, it outperforms all the models with a single classifier in terms of accuracy, precision, recall and F-score.
W3BnNr: An Automated tool for information gathering, vulnerability scanning, attacking and reporting for injection attacks on web application

Web applications must be protected from security threats due to the widespread use of the internet and web applications in today's society. In fact, over 75 percent of currently available online apps have at least one defect, of which 24 percent are regarded as serious flaws. In recent years, the number of web application attacks has increased by 88 percent, more than double the year-over-year growth rate. Injection attacks are the third most serious security risk to a web application, according to OWASP, as 94 percent of apps have been tested for some kind of injection vulnerability. Injection attacks include the attacker sending an application a malicious or untrusted input that the program interprets as a component of a valid query, code, or request. Too many web application vulnerabilities are a result of input validation problems. Unfortunately, these issues arise due to developers or users who are not security-aware, leaving several web applications on the internet vulnerable to attacks. To solve the underlying issue, we have proposed a tool that does automatic crawls, and scans for injection vulnerabilities such as SQL Injection, Cross Site Scripting, etc., which we later use for exploitation and reporting purposes.
Blockchain Technology as a Solution for Vulnerabilities in Internet of Things-A Survey

The Internet of Things (IoT) has got various applications in today's life. In IoT there is central server through which information and data passed between the connected devices. This will create many security and privacy issues. Phishing attack, Denial of service attack, false authentication etc are some of the security issues in IoT systems. To solve these issues, central server can be eliminated and technology like Blockchain, where there is no central authority can be adopted. In this paper various use cases of IoT and Blockchain are studied. IoT is vulnerable to many attacks. These vulnerabilities are studied and how blockchain technology and IoT can be integrated to enhance security are also discussed
A Novel and Robust Breast Cancer classification based on Histopathological Images using Naive Bayes Classifier

One of the most significant problems facing public health today, breast cancer is regarded as the primary reason for cancer-related mortality among females all over the world. Early detection of this condition can significantly help in boosting the likelihood of the patient surviving the illness. In this regard, the gold standard diagnostic procedure is the biopsy, which entails the collection of tissue samples for subsequent examination under the microscope. However, the histological examination of breast cancer is not a simple process, requires a significant amount of effort, and can result in a significant amount of disagreement among pathologists. Pathologists may therefore benefit from the assistance that an automatic diagnostic system can provide in terms of improving the efficiency of diagnostic procedures. In this study, we identified cases of breast cancer by employing the Naive Bayes (NB) method. The implementations of this machine learning method could most certainly help with breast cancer control efforts for identifying, predicting, and preventing the disease, as well as planning for health care.