Reviewerly/exHarmony huggingface dataset:
HealthGuard: Blockchain-Powered Healthcare Data Security

Healthcare information is sensitive and private. Consequently, Users are liable for assuring the confidentiality of their medical records. It is simple to steal, or even eradicate medical data. In that cases the medical data may not be found the same as before. This affects the right treatment to the patient to save the patient’s life. Traditionally, the raw medical data of a patient was preserved in a database and could be hacked by hackers. Medical applications are particularly vulnerable to security concerns including data theft. The security problem in medical applications has been fixed by the Blockchain. Because of the unique features like decentralization, consistency, and cryptography the Blockchain technology is used for storing the medical data securely. It uses shared keys and the data is stored in the form of blocks based on the consensus procedures. The performance of various Blockchains are analyzed for secure medical data storage.
Detection of Deep Fake Images Using Convolutional Neural Networks

Images are frequently manipulated for various purposes, often serving the interests of specific parties. Given that images are commonly regarded as evidence of reality, their manipulation can significantly contribute to the spread of fake news or misleading information. Detecting such image falsifications necessitates access to extensive image data and the development of models capable of scrutinizing each pixel within an image. Furthermore, ensuring efficiency and flexibility in data training is vital to support practical applications. Big data and deep learning concepts, particularly the Convolutional Neural Network (CNN) architecture utilizing Error Level Analysis (ELA), have proven highly effective, achieving a forgery detection rate of 91.83% with convergence in just 9 epochs.
An Ensemble Learning Approach for Classifying Illicit Transactions in Bitcoin

Bitcoin has become a popular method for illegal transactions, such as ransomware payments and money laundering. Detecting these activities within the Bitcoin blockchain is challenging due to the lack of transaction labels and the network's enormous size, allowing bad actors to hide their actions. Previous studies have suggested using unsupervised anomaly detection or supervised and active learning techniques for identifying illicit activity within Bitcoin's network. This paper presents a novel machine-learning methodology that combines feature engineering with supervised learning algorithms to identify illicit transactions in the Bitcoin network. The approach shows promising results in accurately classifying transactions as illicit or legitimate. This method not only provides an efficient solution for detecting unauthorized transactions but also holds significant implications for establishing robust regulatory frameworks for digital currencies.
Dynamic S-Box Generation Algorithm with Improved Strict Avalanche Criterion by Selection of Adjacency Matrix Parameters

This paper covers the problem of generating dynamic S-boxes with robust properties for cryptographic algorithms. Dynamic S-box calculation was performed using affine transformation. The importance of S-boxes, which are used today for modern block encryption, is studied. When calculating S-box values, it is emphasized that their design is robust to various cryptographic properties. The proposed method showed that it is possible to construct dynamic S-boxes resistant to various cryptographic requirements by selecting elements from a directed adjacency matrix. Research has shown that S-boxes generated using this method exhibit 112 nonlinearity with a differential convergence probability of 4/256 and an average strict avalanche criterion (SAC) of 0.5. These indicators show increased resistance to non-linear and other cryptographic attacks. This method contributes to the development of S-box design methodology by providing valuable insights into the design of secure and robust cryptographic systems.
Empowering Early Diagnosis: Optimizers Revolutionizing Breast Cancer Detection with DenseNet

Breast cancer, a highly lethal disease with a significant global impact, requires early detection to reduce its mortality rate. The emergence of pre-trained Deep Learning (DL) algorithms, commonly utilized for object detection, image classification, and image segmentation, has advanced early breast cancer detection. Several researchers have explored early breast cancer detection using pre-trained DL algorithms in conjunction with classifiers or other existing methods. However, existing research lacks a thorough examination of optimizers and their potential influence on the behaviour of these pre-trained algorithms. This study aims to comprehensively investigate the behaviour of the pre-trained DenseNet201 model under various optimizer and learning rate configurations. The analysis reveals that the DenseNet model achieved superior performance (100% accuracy) with the SGD (Stochastic Gradient Descent), and RMSProp optimizers at a learning rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–2</sup> and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–3</sup> , respectively. However, at a learning rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–4</sup> , the Adam optimizer demonstrated the highest accuracy of 99%.
Photonic Artificial Intelligence Issues

This paper addresses the methodological issues of developing a Photonic Artificial Intelligence (PAI) based on full optical technologies for processing continuous (analogue) signals without transforming them into digital form. PAI must ensure overcoming digital computer limits. It can provide low-energy artificial intelligence (AI) and eliminate machine learning time. The analogue photonic way also will help to consider the non-formalisable cognitive semantics of AI models for more representation of human feelings, thoughts, and the transcendental states of mind, which digital computers cannot fully express. New problems are emerging in PAI, including new methods for parallel processing analogue signals, synthesising unique photonic materials for rewritable 3D holographic memory and controlling optical processes with digital computers. The paper suggests a genetic algorithm for synthesising the photonic materials using an inverse-problem-solving approach. This algorithm may also be used to solve computer fluid dynamic tasks quickly.
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting

Recently, various illustrative examples have shown the impressive ability of generative large language models (LLMs) to perform NLP related tasks. ChatGPT undoubtedly is the most representative model. We empirically evaluate ChatGPT’s performance on requirements information retrieval (IR) tasks to derive insights into designing or developing more effective requirements retrieval methods or tools based on generative LLMs. We design an evaluation framework considering four different combinations of two popular IR tasks and two common artifact types. Under zero-shot setting, evaluation results reveal ChatGPT’s promising ability to retrieve requirements relevant information (high recall) and limited ability to retrieve more specific requirements information (low precision). Our evaluation of ChatGPT on requirements IR under zero-shot setting provides preliminary evidence for designing or developing more effective requirements IR methods or tools based on generative LLMs.
A Comparative Analysis of Machine Learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics

Machine learning models have recently developed into a crucial predictive analytics tool, driving progress in a variety of industries. Support Vector Machine (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory are four well-known machine learning algorithms that are thoroughly examined and compared in this work (LSTM). By assessing these models on many parameters, including accuracy, precision, recall, and computing efficiency, the study conducts an in-depth investigation. To ensure the validity and relevance of the findings, a broad dataset is used. The comparison analysis highlights the distinct advantages and disadvantages of each model, providing information about how well suited they are for various applications. The analysis explores efficiency and interpretability as well, offering helpful direction for scholars and practitioners. The work also advances the field of predictive analytics by providing actual data on the effectiveness of these algorithms, paving the path for more in-depth and efficient application of machine learning models across a range of fields.
Digital art scene development strategy and conceptual framework—Coding analysis through artist interviews

With the increasing need for distinctive experiences in public art spaces, the roles of these places are progressively evolving to accommodate the intersection of technical advancements and spectators’ preferences. In light of the emergence of Metaverse Internet 3.0, there is a renewed examination and exploration of the operational framework of public art spaces. This transformation is evident not just in the presentation of artistic works but also in the approaches and techniques employed in urban management.The present study will adopt a conceptual framework that centers around the digital art scene while considering the impact and backing it receives from both the physical public art space and the virtual Metaverse. This framework will offer a more precise comprehension of forthcoming development strategies for the digital art domain. Simultaneously, it will also offer fresh perspectives and insights for urban administrators, enabling them to more effectively adjust to the emerging paradigm of Internetbased intelligent artistic lifestyles.
Cutting-Edge Developments in Deep Learning Applications for Breast Cancer Detection: A Comprehensive Overview

Breast cancer is a common tumor that affects women all over the world. Today, ductal carcinoma is the most prevalent form of breast cancer. People all around the world are really concerned about their health due to this sort of breast cancer. Predictive analytics are crucial for quickly identifying, diagnosing, and developing treatment regimens for persons with ductal carcinoma. The aim of this study is to identify research areas that haven't been sufficiently explored in the existing ductal carcinoma prediction analytic literature. The objective of this study is to review all previous studies on predictive analytics and ductal carcinoma. We intend to identify the present boundaries and gaps in this field of study by carefully examining the prior research. We'll also make recommendations for potential future research areas based on what we've discovered in order to close any gaps and advance the discipline. We will primarily concentrate on carefully selecting the appropriate prediction models, identifying the appropriate components, and assessing the accuracy of these models in predicting outcomes such as recurrence, metastasis, and survival. We will also discuss how crucial it is to investigate ductal carcinoma using an interdisciplinary approach, which entails collaborating with authorities in the domains of oncology, radiology, genetics, and data science. The main goal of the study is to give an in-depth look at the current state of research on predictive analytics for ductal cancer while pointing out possible directions for future research.
Highly Secured Image Fringe Steganography

In the age of modern information technology, information security is a big concern. Steganography is a potent technology that can be used to embed secret data into photos, videos, or audio files without drawing suspicion. It is one of the creative arts of making communication invisible by hiding the information behind the other information or inside the other. Stego” means “Cover”, and “Grapy” means “Writing. Here, a new image steganographic technique of Steganography is proposed. First, we find out the fringes of an image in which we must hide data randomly without affecting or making any changes to the smoother region. The Canny Edge Detection algorithm calculates the cover image's fringe.
Vision-based Autonomous Tracking and Landing Method for UAV on a Dynamic Platform

To improve the accuracy of tracking method and the stability of landing algorithm for UAV on the dynamic platform, a two-stage vision-based autonomous tracking and landing algorithm is proposed. The first stage is target tracking. To complete this purpose, a visual coordinate detection method based on AprilTag is used. And then K-Nearest Neighbors anomaly detection method is used to eliminate the abnormal data to improve the accuracy of velocity estimation approach. After that, the Attenuated Memory Cubature Kalman Filter (AMCKF) is applied on trajectory prediction. The second stage is autonomous landing. In this stage, a multi-sensor data adaptive fusion method is used to obtain the more accurate altitude information. The experiments show that the proposed algorithm can reduce the deviations of position and make the UAV landing process on the platform more accurately.
A Uniform Ensemble Model for Multilingual Recommendation Systems

Incomplete or missing user profiles can disrupt the effectiveness of recommender systems. Therefore, it is vital to establish a uniform ensemble model, which tends to recognize diverse customer shopping intentions. This paper presents our recall models consisting of traditional recommendation algorithms (i.e. itemCF, userCF, swing, etc.), deep graph recommendation algorithms (LightGCN), and embedding recommendations (word2vec embedding, cross encoder). This model is verified via multilingual shopping session datasets, namely Amazon-M2. Within KDD CUP 2023 Tasks 1 and 2, we use the sample data processing and model framework by low-cost devices. On the final leaderboard, the Onewo team is in 19th in Task1 and 21st in Task2. Every strategy is applied to match the top 100 items. Furthermore, we adopt a combination of group and 3 LGB models to re-rank recalled items for every strategy. By strategies, the Mean Reciprocal Rank (MRR) scores for tasks 1 and 2 of phase 2 are 0.33347 and 0.35541 respectively.
A Multi-sensor Data Fusion Method for Vehicles Based on Improved GNN Association

Intelligent driving technology has become a hot direction in the automotive industry. Accurate and stable tracking and fusion of target information from multiple heterogeneous onboard sensors are prerequisites for intelligent vehicles to make correct decisions. To reduce target measurement errors and improve the stability of perception results under the constraints of low computing power of vehicle microcontrollers, this paper proposes a framework for multi-sensor data fusion in vehicles. Addressing the problem of temporary target disappearance due to sensor occlusion and the presence of irrelevant signals and noise, a single-sensor target tracking method based on Kalman filter and the lifespan approach is designed. To overcome the issue of erroneous associations commonly encountered in traditional global nearest neighbor (GNN) methods, an improved GNN data association method is proposed. It involves designing a target correction method for single-sensor tracking, followed by the design of a tracking gate setting method. The association cost is used for secondary target selection within the gate, and a weighted similarity function is defined to establish target associations. Finally, a convex combination fusion algorithm is used to fuse data from multiple sensors.
Renewable Energy Forecasting: a case study of a PV Solar Plant in a Small Island

The increase in power generation from uncertain renewable energy sources combined with the aleatory nature of loads is significantly affecting electrical network management, which aims to find a constant equilibrium between generated and consumed power. These problems are even more significant in geographical islands' networks disconnected from the main grid, where loads suffer a further variability due to the seasonal tourist influx. For these reasons an accurate forecasting of power generation is an essential tool to improve network's management. In this framework, the paper proposes a direct method for forecasting photovoltaic power generation. The method uses Long Short-Term Memory (LSTM) neural networks applied to real power and meteorological data of a PV power plant installed in an Italian small island, named Ustica.
Enhancing Underwater Images Through Non-Local Prior-Based Dehazing

Underwater inspections, marine biology, and archaeology have all struggled with the same problem: poor picture quality caused by haze and colour distortion. This study explores the potential of dehazing methods based on non-local priors for improving underwater photography. Diverse underwater pictures were carefully acquired and pre-processed to illustrate a range of hazy and environmental situations. Dark channel prior, multi-scale retinex, convolutional neural networks (CNNs), and other dehazing techniques were used and analysed thoroughly. Standardised quantitative criteria, including PSNR and SSIM, confirmed the dramatic improvement in picture quality brought about by these dehazing methods. In particular, CNN-based techniques showed impressive performance, with high PSNR and SSIM values being attained. Qualitative evaluations also showed how underwater photographs were changed in appearance, with missing features being restored, contrast being boosted, and colour distortion is minimised. These results highlight the promise of dehazing methods based on non-local priors as useful resources for improving marine photography. They provide light on cutting-edge practices, which have real-world consequences for ocean exploration and study. Possible future work to better understand the ocean may entail tailoring these methods to various water conditions and incorporating them into real-time imaging systems.
Bottom-Up Human Pose Estimation Based on Multiple Anchor Points Regression

In the domain of human pose estimation, the combination of a bottom-up paradigm and regression-based methods has an advantage in terms of efficiency, yet suffers from sub-optimal performance. To booster the regression procedure with more spatial information and thereby enhance the accuracy of keypoint detection, we further investigate the pixel-wise regression approach through the introduction of multiple anchor points for offset regression. Training and evaluation were performed on the MS COCO dataset. The results of our experiments demonstrate that employing multiple anchor points improves the accuracy of human keypoint detection, with a competitive outcome of 69.4 AP on the COCO validation set for multi-scale testing without further refinement. Additionally, our approach is highly efficient in terms of computations and has lightweight model parameters.
Multi-scale Depth-guided Instance Segmentation Based on Attention Mechanism

Instance segmentation plays an important role in identifying and locating objects in autonomous driving tasks. However, existing instance segmentation methods still have problems in scenarios of long distance and similar texture, which typically yield missing prediction or failures like boundary blur. To this end, we propose the Depth Guidance Module (DGM) which deploys an auxiliary depth estimation task to assist the existing instance segmentation approach. The estimated depth features obtains rich scene structure information to guide the target detection and segmentation in the challenging scenarios mentioned above. To make use of multi-scale features, we further propose the Multi-scale Depth-guided Cascade Mask RCNN (Multi-DG-CMR) by integrating both the DGM and the Feature Pyramid Network (FPN) module. Experiments show that our model performs better than the classic instance segmentation models on both validation and test set of Cityscapes benchmark.
Financial Fraud Detection and Comparison Using Different Machine Learning Techniques

More than ever before, fraudsters are actively targeting financial transactions. This research paper examines the effectiveness of various machine learning techniques in detecting and preventing financial fraud arising due to transactions. In this paper we have compared and analysed 6 different kinds of Machine Learning Techniques i.e. (Naive Bayes, Neural Network, Decision Tree, Support Vector Machine, Logistic Regression, Random Forest) with Random Forest being the most suitable for predicting fraudulent transactions. The research also identifies patterns in fraud cases, as the timing of occurrences and the demographics targeted. The study concludes by suggesting future research directions, including exploring advanced ensemble learning methods, incorporating deep learning algorithms, addressing imbalanced datasets, implementing real-time fraud detection, and extending the research to other sectors such as health. Overall, this study helps in understanding of credit card fraud detection and provides valuable insights for future research.
FRAMEWORK FOR EVALUATION OF PROCEDURES FOR HDR LUMINANCE IMAGING MEASUREMENTS

Luminance distribution measurements with high dynamic range (HDR) are required for various applications (e.g. measurement of new LED- or laser-based car headlights, obtrusive light and glare evaluation of indoor and outdoor scenes) where high contrast levels exist simultaneously in one scene. The overall goal of this work is to develop a methodology to evaluate by simulation the performance of different HDR image algorithms to provide accurate luminance measurements of those high-contrast scenes, when applied to luminance measurements from different cameras.
High-Dimensional Mixed-Data Regression Modelling Using the Gifi System with the Genetic Algorithm and Information Complexity

This paper presents high-dimensional mixed-data regression modelling using the Gifi (1990) system along with the genetic algorithm (GA). Information complexity (ICOMP) and AIC-type criteria are derived and provided as the fitness functions to choose best subset of predictors. Statistical analysis and modelling of mixed data sets have been a challenging task for many practitioners and researchers for a long time. The usual classical statistical procedures for mixed data sets often fail to produce good results since most of these procedures assume that the underlying data is purely continuous even with the presence of categorical variables in the model. In this paper, we introduce and use Gifi (1990) system to transform the mixed data to a continuous space and perform a regression analysis and modelling currently faced problem in data science and machine learning methods. We illustrate the flexibility and the utility of our proposed approach on two real mixed data sets in performing multiple regression model and choosing the best subset of predictors.
Discrete Functional Data Analysis Based on Discrete Difference

Functional data analysis has been proposed by Ramsay and Silverman, and since then, many theories and methods have been developed. We have already proposed discrete functional analysis (Mizuta 2006). However, in this paper, we refine it and provide a theoretical explanation of uncorrelated discrete differences. An effective approach in functional data analysis is the use of differentiation of continuous functions, especially by expressing the data set in terms of differential equations, which allows us to explore the structure of the data set. The same approach for discrete functions requires the consideration of the discrete difference of discrete functions, which corresponds to the derivative of a continuous function. Problems arise when ordinary discrete differences are used for data analysis. We show this problem and propose an improvement of the uncorrelated discrete difference. We also show how to use the uncorrelated discrete difference.