Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Deep Learning Pre- Trained Models and Machine Learning Classifiers for Accurate Cervical Cancer Diagnosis using Colposcopy Images

Cervical cancer is a significant public health issue worldwide, and early detection is essential for successful treatment. Colposcopy is a widely used diagnostic tool for cervical cancer, but its accuracy depends on the examiner's experience. Computer-aided diagnosis (CAD) systems can potentially improve diagnostic accuracy and reduce variability among examiners. The proposed system includes feature extraction using different deep-learning pre-trained models and classification into cancerous and non-cancerous images using different machine-learning classifiers. The efficacy of the suggested system is assessed utilizing a conventional dataset comprising colposcopy images. The results of study demonstrate a potential of CAD systems for improving accuracy of cervical cancer diagnosis and reducing inter-observer variability using deep learning. The paper presents that the Googlenet and ResNet-50 pre-trained models features from transformation zone extracted colposcopy images have the highest classification accuracy of 76.64%.
Perceptual Based Sentiment Analysis of Consumer Reviews using Rational Machine Learning Techniques for E-Commerce Applications

The growth of e-commerce has led to a surge in online shopping causing people to share their reviews and opinions on the product purchased. To help companies identify negative comments, the study uses natural language processing (NLP), K-means clustering, and sentiment analysis on a dataset of product reviews. The goal was to generate graphical and extractive summaries that highlight aspects that contribute to product failure. The system computed the sentiment polarity of each aspect and produced opinionated summaries. The system was evaluated on multiple review datasets and outperformed a baseline approach using Support Vector Machine (SVM) in terms of precision and recall.
EGFD-AROMA-based Pattern Forecasting with Crime Rate Level Identification

In numerous cities, crime has become a chief concern that leads to the rising demand for timely predicting citywide crime occurrence. Therefore, this paper proposes an efficient forecast pattern with crime rate level identification. Primarily, the input crime video undergoes preprocessing. Afterward, the features are extracted. Then, by utilizing Permuted Congruential Generator-based Northern Goshawk Optimization (PCG-NGO), the important features are selected. Thereafter, feature similarities are identified. Conversely, the attributes are extracted from the crime detail dataset. Next, by considering the selected features, identified similarities, and extracted attributes, the crime categories are clustered using the JaroWrinkler distance-based K-Means Algorithm (JW-KMA). Then, the dependence strength is analyzed for the clustered categories. Lastly, for predicting patterns and crime rate levels, a Fuzzy Inference System and Convolutional Encoder Decoder-based Residual Neural Network (FIS-CEDRNN) is employed. By utilizing the extracted attributes from the crime detail dataset and the predicted patterns, Entropy Generalized Finite Difference-based Autoregressive Integrated Moving Average (EGFD-ARIMA) is trained for forecasting crime patterns. The experimental outcome exhibited that the proposed technique withstands maximum accuracy when contrasted with existing methodologies.
Classification and Detection of Blood Subtype Cells Images using Hybrid Convolutional Neural Network

Today, people’s personal health is mainly monitored through blood parameters. The increase in white blood cells will lead to more complicated, having four classes in white blood cells are Eosinophil, Lymphocyte, Monocyte and Neutrophil. Each subtype having some range, if it exceeds that range, we get infected. The proposed hybrid CNN model that evaluates the white blood cell in the image collected through different data sources. It identifies the White Blood Cells present in the blood cell image and highlight the cell and also clarifies the presence of Eosinophil, Lymphocyte, Monocyte and Neutrophil. The analysis is made on precision, recall, f1-score, accuracy to evaluate the performance of the model. Our proposed model proves to be ahead than the other model proposed in the previous work.
Combining Demographic Tabular Data with BERT Outputs for Multilabel Text Classification in Higher Education Survey Data

Institutions of Higher Education (HEI) often possess rich text data in the form of student surveys. However, because these data are expensive to process, many universities have not yet capitalized on this resource. When working with student text data, researchers often desire to first label student responses with common categories of interest, a task in Natural Language Processing known as Multi-label Text Classification (MLTC). BERT and other Large Language Models have produced state of the art results on MLTC tasks; yet because MLTC generally presents challenges of data scarcity and data sparsity, accuracy often remains too low to fully automate the task. Unlike many common MLTC datasets, these student survey data can usually be paired with rich tabular data, both academic and demographic. In this paper, we show that a fusion approach combining tabular data with BERT outputs derived from student responses significantly improves model performance, increasing label ranking average precision from.75 to.84. The paper thus contributes to the open academic discussion of whether fusing tabular demographic data with BERT outputs improves performance, and also offers a practical approach for HEIs to automate survey labeling and thus incorporate more student text data into institutional research.
Online Forecasting Based Anomaly Detection For Monitoring Large Scale Streaming Data

Anomaly detection for time series data is critical for monitoring the status of ever-growing data sources, e.g., health metrics of data servers. An important element of anomaly detection is time series forecasting, which is often the most time-consuming stage in the system. Existing approaches based on batch processing usually take several seconds or more to process one forecasting task, thus not applicable for large-scale real-time applications. In this paper, we present an online-learning based forecasting algorithm to address the computation bottleneck. It readily handles missing observations and has a constant time complexity, independent of the number of past observations. Our experiments show that it achieves similar level of accuracy as existing approaches while only takes a fraction of computational resources. The proposed algorithm can help us easily monitor tens of thousands of data sources simultaneously with only a small hardware cost.
Analyzing Data Sets for ML-driven Fraud Detection in SAP Systems

Enterprise Resource Planning (ERP) systems are used by companies to support and automate business processes. Users need to be granted the necessary permissions to be able to perform their work. Following the principle of least privilege, these permissions shall restrict the access to such information and resources only, which are required to complete the tasks involved. However, even using a well-attuned authorization concept, some users may still misuse the ERP system to enrich themselves. Besides a reduction in profit, companies suffer a loss of reputation and trust from their stakeholders. Furthermore, they may be faced with lawsuits from aggrieved customers or suppliers. Such occupational fraud shall therefore be traced and tracked down. Since all business operations are recorded within an ERP system, a variety of different data sources is available. This paper explores the wealth of data sources found in SAP ERP, the most widespread ERP system from SAP, the world’s leading vendor of ERP systems. It examines, in how far such data sources are suited for fraud detection. Previous literature is surveyed. It turns out that the applicability of the data used in previous work is limited or that no suitable data sources are available for developing and evaluating fraud detection techniques. Therefore, this paper proposes three data sets extracted from SAP ERP. The data sets are made available via GitHub and machine learning techniques are applied to evaluate the adequacy for fraud detection. In addition, the feature importance is examined to increase the transparency of fraud detection.
Mutually Exclusive Learning for Generators with Multi-Label Classifiers

In this work, we introduce the idea of “mutually exclusive learning” and formulate it as a multi-label classification problem. We provide two implementations of the mutually exclusive learner; the first using a consensus based multilabel framework and the second using an adaptive multi-label framework. We use the mutually exclusive learning paradigm for designing both generators and discriminators (classifiers). We experiment with the MNIST dataset for both classifying the digits using a mutually exclusive classifier and generating handwritten digits using a generative adversarial network (GAN) trained with a mutually exclusive discriminator implementation. Our results establish that GANs trained with a mutually exclusive discriminator converge faster than the corresponding GAN with a standard discriminator. Furthermore, the quality of the generated images is also visually better than those generated by a regular GAN.
Comparison of SQL, NoSQL, and NewSQL Database Technologies

IoT generates a large volume of data in different formats. Exponential data generation has resulted in challenges in capturing, managing, storing, analyzing, and visualizing data. This necessitates the implementation of exemplary database architecture to store data. The paper aims to evaluate the various database architectures.
Smart Bitcoin Alert System

Bitcoin, a decentralized digital currency, operates without the involvement of traditional financial institutions. Bitcoin transactions are conducted directly between parties, without the use of intermediaries, thanks to blockchain technology. Wallets, public keys, and private keys are necessary for the secure transactions of this cryptocurrency. Transaction can take place using Bitcoin simply going via centralized exchanges, in contrast with numerous other cryptocurrencies. This paper explores the unique features of Bitcoin and its advantages over other crypto assets. Notably, the decentralized and distributed ledger of blockchain technology ensures the secure storage of verified bitcoin transactions by network nodes. This distinguishes Bitcoin from assets that rely on centralized exchanges for verification. Recognizing the growing acceptance of Bitcoin in various transactions, including those conducted by small businesses, this research focuses on the need for accurate early prediction of Bitcoin prices. The study proposes leveraging machine learning algorithms, specifically Random Forest and Deep Learning (Long Short Term Memory), to predict the open and close values of Bitcoin. This predictive analysis aims to assist investors in making informed decisions and optimizing their Bitcoin investments. Accurate early forecast of Bitcoin prices is a need, as this research highlights, given the increasing use of Bitcoin in a variety of activities, including small company transactions. In order to forecast the open and closing prices of Bitcoin, the study suggests using machine learning techniques, notably Random Forest and Deep Learning (Long Short Term Memory). By using predictive analysis, investors may maximize their Bitcoin investments and make well-informed judgements. The study emphasizes the importance of accurate price predictions for Bitcoin investors and introduces machine learning algorithms as effective tools for achieving this goal. A comparative analysis of Random Forest and Long Short Term Memory algorithms will be conducted to evaluate their accuracy in predicting Bitcoin prices. The research aims to provide investors with valuable insights into optimizing their investment strategies based on reliable early predictions of Bitcoin values. This idea is reliable using Machine Learning which provides effective results. By Comparing the accuracy between two algorithms the prediction of bitcoin will be implemented. This can be done using Machine Learning Algorithm (Random Forest) and Deep Learning Algorithm (Long Short Term Memory). This prediction will help the bitcoin investors to identify the open and close value of the bitcoin so that the investors can invest their bitcoin in an efficient manner and get benefited.
Fake Currency Detection using Image Processing

Advancements in color printing have led to a rise in the number of counterfeit currency notes being created and duplicated on a massive scale. In the past, it was very difficult to do this, but now anyone with a laser printer can easily do it. Due to the rise of fake notes, the number of counterfeit currency notes has increased significantly in India. This is a major issue that the country is facing due to its various problems such as black money and corruption. A system that can detect the fake currency notes in a fraction of the time is being developed. The proposed system will use image processing techniques to verify the authenticity of Indian currency notes. This method will involve extracting various features from the notes. Some of these include the Bleed Line, Water marking, Fluorescence, Security thread, Micro Lettering, and Identification mark. The proposed system is using software known as MATLAB. The Support vector machine is a supervised learning model that is used for extracting the notes' features. It is also used for analyzing data related to regression and classification. We have also tested the other methods related to this proposed work, such as the graphical black Box algorithm. The proposed system has various advantages, such as its high-performance speed and simplicity. It will be able to determine if the currency notes are real or fake. Its accuracy is also measured using the Confusion Matrix.
Hybrid Quantum Computing and Decision Tree-Based Data Mining for Improved Data Security

Data mining and quantum computing have become potent methods for analyzing massive datasets and gaining insightful conclusions. However, conventional techniques for data mining are susceptible to quantum attacks, necessitating the development of quantum-resistant methods like lattice-based cryptography. This research provides a hybrid strategy for better data analysis and security that blends quantum computing with decision tree-based data mining. The suggested algorithm was tested against various datasets, and the findings indicated that it provided substantial protection against quantum assaults while achieving great accuracy and precision in data analysis. On the datasets tested, the suggested algorithm specifically attained an average precision of 95.3% and an accuracy of 96.5%. Additionally, the algorithm resisted quantum attacks, maintaining accuracy and precision subjected to the most extreme quantum attacks. Overall, the evaluation findings show how effective and efficient the suggested hybrid technique is for data analysis and protection, providing a workable answer for practical post-quantum applications.
Comparative Analysis and Performance Metrics Evaluation of Image Steganography Embedding Algorithms

Ensuring secure data transmission and communication over the Internet is of paramount importance, particularly when dealing with private and confidential information. In communication networks, cryptography and steganography are two essential techniques employed to enhance data security. Cryptography acts as a fundamental component in safeguarding data by encrypting it to protect against unauthorized access, while steganography hides secret messages within cover media, deceiving eavesdroppers and providing an effective solution for protecting communication from attackers. When using image steganography, selecting an appropriate cover image and accurately embedding the secret message's position is crucial for its effectiveness, ensuring the concealment of communication traces. Through a comparative analysis such as PSNR, MSE, RMSE, and FSIM, this study aims to offer valuable insights for future scholars concerning how diverse cover media impact the performance of different algorithms. The ultimate goal is to assist future researchers in the development of algorithms that facilitate secure data-sharing communication.
The Intersection of AI and Blockchain in Education: A Bibliometric and Thematic Analysis

Two cutting-edge technologies that are revolutionizing the education industry are artificial intelligence (AI) and blockchain. The study on AI and blockchain in education is bibliometrically analyzed in this paper, emphasizing topics, frameworks, and potential future research areas. The analysis uses the Scopus database to pinpoint essential works and sketch out the conceptual framework of this field of study. The findings show an increasing interest in applying AI and blockchain technology in various educational settings, from K-12 to higher education, as well as the various research paradigms and methodologies used in this field. Studies emphasize creating knowledge management tools, learning algorithms, and decision support systems to strengthen the educational process and enhance learning results. The study also emphasizes the growing demand for cutting-edge technologies like deep learning and the Internet of Things. This paper provides a comprehensive evaluation of research on AI and blockchain in education by highlighting key ideas and frameworks that have been applied in this area. The results offer relevant information on the current level of research in this field and suggest potential directions for advancement.
Research on Classification Method of Broken Pottery in Gansu Province by GNN

In this thesis, the effect of research on the classification Method of Broken Cultural for the fragments in which of Plained Pottery on Majiayao culture (PPMC) on Shannashuzha of Minxian in Gansu Province and Plain Pottery on Qijia Culture (PPQC) on qijiaping village of Guanghe county were modeled and analyzed by SVM, GA-BPN and GNN. The inputter layer are position and characteristics for the fragments of PPMC and PPQC while the outer layer is property. The results show that the optimal network architecture is considered to be 8-9-1 with momentum factor 0.8 by SVM, which the Accuracy-Avg is 56% and the time is 1532s.The optimal network architecture was considered to be 8-10-1, and the optimum values of momentum factor is 0.8 and which the Accuracy-Avg is 67% and the time is 145s by GA-BPN. The iteration on initial population of Genetic Algorithm for BPN was chosen to be 100. The iteration of crossover was set to be 0.8, meanwhile the the iteration of mutation is 0.08, respectively. The optimal network architecture is considered to be by GNN, which the Accuracy-Avg is 91% and the time is 121s.The model can also be used as a guide to evaluate and predict the effect of research on the classification Method of Broken Cultural on the fragments of PPMC and PPQC were modeled and analyzed by GNN for the historical and cultural resources in Gansu Province.
Robust Fraud Detection via Supervised Contrastive Learning

Deep learning models have recently become popular for detecting malicious user activity sessions in computing platforms. In many real-world scenarios, only a few labeled malicious, and a large amount of normal sessions are available. These few labeled malicious sessions usually do not cover the entire diversity of all possible malicious sessions. In many scenarios, possible malicious sessions can be highly diverse. As a consequence, learned session representations of deep learning models can become ineffective in achieving a good generalization performance for unseen malicious sessions. To tackle this open-set fraud detection challenge, we propose a robust supervised contrastive learning based framework called ConRo, which specifically operates in the scenario where only a few malicious sessions having limited diversity is available. ConRo applies an effective data augmentation strategy to generate diverse potential malicious sessions. By employing these generated and available training set sessions, ConRo derives separable representations w.r.t the open-set fraud detection task by leveraging supervised contrastive learning. We empirically evaluate our ConRo framework and other state-of-the-art baselines on benchmark datasets. Our ConRo framework demonstrates noticeable performance improvement over state-of-the-art baselines.
Ontology-Based Generation of Data Platform Assets

The design and management of modern big data platforms are extremely complex. It requires carefully integrating multiple storage and computational platforms as well as implementing approaches to protect and audit data access. Therefore, onboarding new data and implementing new data transformation processes is typically time-consuming and expensive. In many cases, enterprises construct their data platforms without a clear distinction between logical and technical concerns. Consequently, these platforms lack sufficient abstraction and are closely tied to particular technologies, making the adaptation to technological evolution very costly. This paper illustrates a novel approach to designing data platform models based on a formal ontology that structures various domain components into an accessible knowledge graph. We also describe the preliminary version of AGILE-DM, a novel ontology that we built for this purpose. Our solution is flexible, technologically agnostic, and more adaptable to changes and technical advancements.
FedCIO: Efficient Exact Federated Unlearning with Clustering, Isolation, and One-shot Aggregation

Data are invaluable in machine learning (ML), yet they raise significant privacy concerns. In the real world, data are often distributed across isolated silos, challenging conventional ML methods that centralize data. Federated learning (FL) offers a privacy-preserving solution that enables learning without direct data transfer. Meanwhile, the “right to be forgotten” sparks privacy-preserving methods from another viewpoint as machine unlearning, enabling data owners to erase specific data contributions from ML models. However, the invisibility of data in FL scenarios complicates effective local data removal, necessitating tailored unlearning algorithms for FL. Existing federated unlearning methods fall into approximate unlearning, leaving residual memorization of target data, consequently diminishing user trust. To bridge this gap, we propose FedCIO, a novel framework for exact federated unlearning, designed to efficiently manage precise data removal requests in FL scenarios. Specifically, the framework involves client clustering, isolation among clusters, and one-shot aggregation of cluster models. This framework facilitates efficient unlearning by retraining only a relevant model subset rather than from scratch. To enhance the capability to handle Non-Independent and Identically Distributed (Non-IID) data, we further introduce an advanced spectral clustering implementation based on model similarity for better cluster partitioning. Comprehensive evaluation across common FL datasets with varied distributions demonstrates the superior performance of our proposed framework.