Reviewerly/exHarmony huggingface dataset:
Blockchain-IoT Enabled Smart Metering System

The Smart Metering System is a trending technology that is known widely in recent years as one of the topics related to Smart Grids (SG). It refers to the usage of a smart meter with a pre-defined and designed architecture to measure, collect, and analyze the energy consumption of customers in real time. The smart metering system contains some components that are connected to provide the desired application. The basic component is the smart meter that is related to energy customers and connected by a gateway to the utility supplier database. Which allows system measuring and monitoring of energy consumption remotely. One of the important aspects of a smart metering system is data security, where a smart metering system is providing service for a large number of customers and it collects very sensitive data of a customer's energy consumption and other private information that must be secured. To address this, researchers improved different security solutions to preserve the security of smart metering systems taking in mind protecting customers' data and utility network depending on the architecture of the system and desired application. Internet of Things (IoT), Blockchain, Lightweight algorithms, machine learning, and others are used as security-providing solutions for smart metering systems. Therefore, in this study, we presented a review of the recent solutions that provide security for smart metering applications by integrating the smart metering system with different technologies, we also propose a new smart metering system that enables Blockchain technology in order to provide system monitoring and security preserving.
Credit Card Fraud Detection Using Machine Learning: A Comparative Study of Ensemble Learning Algorithms

The enormous increase in credit card usage for both online and offline transactions have resulted in a similar increase in fraudulent activity, elevating credit card fraud to a serious worry for both financial institutions and consumers. This research endeavour entails an examination of conventional machine learning (ML) techniques, including decision tree, logistic regression, support vector machine, and Naive-Bayes, in conjunction with ensemble learning methodologies, such as XGboost, random forest, voting, gradient boosting, Adaboosting, and stacking, for the purpose of identifying fraudulent credit card transactions. The findings indicate that the random forest and XGBoost algorithms exhibited remarkable efficacy, attaining notable degrees of precision and F1-score. The study highlights the importance of utilising anomaly detection techniques in identifying fraudulent transactions. Moreover, it showcases the effectiveness of ML algorithms in augmenting the security of online transactions. Financial institutions can mitigate customer financial losses by employing real-time fraud detection through sophisticated ML methodologies. The research provides significant perspectives on the effectiveness of diverse ML algorithms in identifying credit card fraud and highlights the potential for further progress in this field.
Detection of Landing Gear Damage on Cessna C208B Aircraft using Case-Based Reasoning

Landing gear is one of the main components of an airplane. Landing gear system is one of the important systems in modern aircraft. According to an investigative report by the Federal Aviation Administration (FAA), the majority of landing gear failures are due to failure of the retraction mechanism, where failure of the lock mechanism is common. To find out the symptoms of aircraft landing gear damage, special expertise is needed from technicians who are experienced with model Case Based Reasoning (CBR) to build a text-based airframe damage consulting system with Vector space model and cosine similarity, in evaluating computational case similarity, a similarity function is formed to compare current cases with old cases. The case base reasoning approach can be applied in text-based consulting by looking for the similarity of each text based on the weight value obtained from the SVM calculation results so that the final calculation with cosine similarity is better with the percentage of similarity reaching above 80%.
HSV-Net: A Custom CNN for Malaria Detection with Enhanced Color Representation

Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the HSV color space. HSV is used since of its superior ability to speak to image brightness, at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focuses were used to deliver the classification. The highlights gotten to be forbidden, and a few more clamor sorts are included to the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.
Solar PV Azimuth Angle Modeling Based on Eight Cardinal Directions Using a Support Vector Machine

The inclination of the azimuth angle greatly affects the output of PV solar power. The precision of the rectangular angle of the sun towards the field of PV modules is something that must be considered when installing PV. Previous researchers have made various calculations using a variety of methods. Determining the correct angle of azimuth requires complex mathematical calculations and knowledge of related points of location. This study presented a different perspective on determining the azimuth angle of PV solar installations. Using a support vector machine algorithm (SVM), machine learning is used for modeling solar PV azimuth angles. The azimuth angle with the eight cardinal directions is tried and combined to produce optimal output. Azimuth angle study data is used as data training and testing for modeling. Modeling results using SVM on radial kernel functions with a value of 1.0 and a tolerance value of 0.0 yield a performance root mean squared error (RMSE) of 0.051. The resulting studies are worthy of application and can help facilitate the design and planning of PV solar installations.
Povm-Based Quantum Self-Attention Neural Network

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset.
An Efficient Anaglyph 3D Content based Video Retrieval using Watermarking Technique

Digital watermarking challenges are multiple. The most important are copyright protection, integrity control, and indexing. The latter has gained significant attention from the research community due to the increasing production of multimedia data. Existing anaglyph 3D video watermarking methods are developed mainly for copyright protection applications but they have not targeted indexing applications that facilitate the retrieving of anaglyph 3D videos in databases. Hence, it is necessary to develop a robust watermarking technique dedicated to indexing in order to both protect and facilitate retrieving anaglyph 3D videos. This paper presents a novel watermarking-based retrieval system for anaglyph 3D videos. The system utilizes a mosaic image generated from the original video as the signature, providing a comprehensive representation of the video’s content. To handle the large size of the mosaic image, it is divided into a set of vectors. The obtained vectors are embedded into different selected frames using a dynamic insertion key to ensure visual quality and robustness against collusion attacks. During retrieval, the extracted signatures from the database are compared with the query video’s mosaic image using a similarity measure. Experimental results demonstrate high visual quality, robustness, and effectiveness of the proposed system.
EXplainable Artificial Intelligence (XAI) for MRI brain tumor diagnosis: A survey

The results of the Deep Learning (DL) are indisputable in different fields and in particular that of the medical diagnosis. The black box nature of this tool has left the doctors very cautious with regard to its estimates. The eXplainable Artificial Intelligence (XAI) recently seemed to lift this challenge by providing explanations to the DL estimates. Several works are published in the literature offering explanatory methods. We are interested in this survey to present an overview on the application of XAI in Deep Learning-based Magnetic Resonance Imaging (MRI) image analysis for Brain Tumor (BT) diagnosis. In this survey, we divide these XAI methods into four groups, the group of the intrinsic methods and three groups of post-hoc methods which are the activation based, the gradientr based and the perturbation based XAI methods. These XAI tools improved the confidence on the DL based brain tumor diagnosis.
A Bayesian Structural Time Series Approach for Forecasting Photovoltaic Power Generation

As a renewable energy source, photovoltaic (PV) power generation is becoming increasingly essential in power systems. Accurate forecasting of PV power output is crucial for effective regulation and control of these systems. This paper explores the applicability of the Bayesian structural time series model for PV power generation forecasting using causal inference techniques. The research examines various factors that influence PV power generation and assesses their causal relationships. The developed forecasting model for PV power generation integrates the Bayesian structural time series approach. The accuracy of the proposed model is validated through case studies, demonstrating the successful incorporation of causal inference in PV power generation prediction. This study lays the foundation for future research on utilizing causal inference to evaluate the impacts of specific system changes.
Neural Network-based Real-Time Recognition of American Sign Language Finger-Spelled Gestures: Bridging Communication Gaps

In this work, an innovative solution addresses the limitations of sign language communication, arising from a lack of familiarity and interpreter availability, by leveraging advanced neural networks for real-time recognition of finger-spelled gestures in American Sign Language. This approach bridges the communication gap by providing a means to decode sign language gestures, regardless of the user's familiarity with sign language, through a two-step process involving gesture refinement and classification. The ultimate objective is to create a model capable of seamlessly translating live video feeds of American Sign Language hand gestures into on-screen text representations of specific signs. Notably, our meticulously trained CNN classifierachieves an impressive 95.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy in recognizing 27 symbols, including the 26 English alphabets and a ‘blank’ symbol usedfor sentence spacing.
QoS Based Virtual Machine Consolidation for Energy Efficient and Economic Utilization of Cloud Resources

As Cloud computing is highly demanded in the business industry and rapidly growing, to fulfill Energy Efficiency, Service Level Agreement (SLA), and efficient resource utilization become the challenging task to protect the Quality of Service (QoS). However, VM consolidation is the primary solution to this issue. Inappropriate live VM migrations in VM consolidation itself expensive and can degrade the performance and violate the QoS in heterogeneous cloud environment. To address this issue, in this model, evolved multi objective based SLA and Power aware VM consolidation SLPA-VMC technique corresponding to various phases in VM consolidation. In this approach, past usage pattern based method underutilized and over utilized (UPbUU and UPbOU) is developed to detect the underutilized and over utilized hosts for an economic utilization of resources. In addition, suggested SLP ABFD (SLA and Power aware based Best Fit Decreasing) and the Least Allocated-Maximum Requested (LA- MR) based technique managed the affected migrated VMs on the appropriate host to maintain the QoS. To validate the suggested techniques, the experiment was conducted on dynamic workloads in cloudsim. The experimental results show that suggested techniques can significantly outperform other traditional methods regarding the SLA violations and an efficient utilization of cloud resources.
Prediction of Photovoltaic Power Generation Based on SCC-CNN-GRU

A short-term combined prediction model based on a convolutional neural network with Spearman's correlation coefficient method and a deep learning network with gated recurrent units is proposed to address the problem of inaccurate prediction due to a large number of influencing factors of photovoltaic power generation under complex conditions. That is SCC-CNN-GRU model. The weather data such as insolation radiation and temperature are used as input quantities and the dimensionality of the weather data entered into the forecast model is reduced, and then the correlation between each weather data and photovoltaic power generation is obtained by using the Spearman correlation coefficient method, and the redundant components with low correlation are removed. The existing bad data are then optimized and the input data are normalized, followed by a convolutional neural network approach to realize the refinement of these data features and input them into a gated recurrent network to achieve accurate prediction of photovoltaic power generation. The accuracy of the combined SCC-CNN-GRU prediction model is examined by comparing it with two single prediction models, LSTM and GRU. After the comparison of the results, the SCC-CNN-GRU-based photovoltaic power generation's prediction model in this study can ensure that the predicted values are closer to the actual values and have higher prediction accuracy under the complex and variable conditions.
A Novel Technique for Handwritten Text Recognition Using Easy OCR

Among computer vision's many new subfields, character recognition is one of the most promising. Humans' superior recognition skills make them the most powerful species. Humans have little trouble deciphering the hand transcription. There are distinct patterns to look for in various languages. The text is easily recognizable by humans. The machine cannot recognize the manual transcription. The system needs help recognizing the text. Processing the input image, extracting features, and training a classification schema are all part of the text recognition process. The computer is taught to look for similarities and differences between different handwriting samples in this method. The program reads a picture of a handwritten transcript and creates an electronic version. Today, cutting-edge scientific methods expand humanity's horizons in all technological endeavors. Character recognition, often known as OCR (Optical Character Recognition), is one example of such a field. The approach presented here is built on EasyOCR and Regular Expressions, which facilitate exporting trip sheet information to CSV. The suggested model divides the image into many boxes based on the intersection of lines and then looks for individual characters within each box. Recognized texts are saved independently, allowing for format conversion at a later date. Unlike other types of OCR (Optical Character Recognition), which focus on recognizing characters generated by a computer or typewriter, this project focuses on developing an algorithm for recognizing handwritten characters, commonly known as HCR (Handwritten Character Recognition). An innovative method, based on Convolutional Neural Network (CNN) and including character feature extraction approaches, is proposed for recognizing English language characters. Over 90% of the time, CNN correctly identified the characters being shown.
An Entity Alignment Method Integrating Dual Entity Classification and Graph Attention Networks in Power Domain

In the power domain, there are numerous businesses, massive data, and intensive knowledge. With the construction of smart grid projects, there is a need for effective utilization of power knowledge. In this situation, knowledge graphs have been widely applied to various businesses in the power domain. However, there are a large number of synonymous entities in graphs of different business areas, which is not conducive to the integration of power knowledge and is a major obstacle to building a large-scale, generic power knowledge graph. Entity alignment is the process of identifying all entities in different graphs that point to the same object in the real world. Excellent entity alignment technology is essential for constructing a comprehensive, accurate, and high-quality knowledge graph in the power domain. In this paper, we propose an entity alignment method that integrating dual entity classification and graph attention networks. The method first categorizes the entities in the power knowledge graph into multiple sub-graphs based on their functional differences and the types of attribute values they contain. Using graph attention network frameworks on the sub-graphs formed by the dual entity classification, the model can capture the complex dependency relationships between entities and reasonably calculate the weights of each neighboring entity when aggregating entity features. We validated the effectiveness of our method on a power dataset, and it achieved a 6.5% improvement in Hits@l score compared to the best-performing method among all the comparative methods.
Malicious URL Analysis Using a Suite of Machine Learning Techniques

Recognizing and classifying dangerous URLs is essential for assuring user safety in light of the exponential growth of Internet usage. Numerous cyber threats, such as phishing, malware distribution, and identity theft, are enabled by malicious URLs. This research seeks to enhance user safety by developing a classification algorithm based on machine learning that can differentiate between malicious and secure URLs. A massive database of potentially dangerous and secure URLs is analyzed in the first stage of the research process. The URLs are preprocessed to eliminate redundant information and extract pertinent properties (such as length, symbols, numerals, and characters). Frequently, the dataset is divided into training and test sets so that various classifiers can be compared directly. This study uses six distinct machine learning algorithms to detect and classify malicious URLs: Random Forest, XGBoost, Support vector machine, convolutional neural network, recurrent neural network, and artificial neural network. The results that cast light on the algorithm's performance are accuracy, precision, recall, and F1 scores. The study discovered that the recommended CNN model distinguished malicious from secure URLs with a success rate of 99.9%. Examining criteria such as URL length and symbol usage significantly improves classification accuracy. The research also examines whether URL IP addresses can be used as indicators of malicious intent. This research's findings can be applied to detecting and preventing intrusions employing malicious URLs. This study arrived at the chosen categorization approach through comparison; it will serve as the basis for future research and real-world implementations in cybersecurity. Increases user security against cyberattacks and data intrusions by precisely labeling and detecting problematic URLs.
Geospatial Analysis and Mapping of Natural Disasters in Situbondo, Indonesia: A Simple Additive Weighting (SAW) Approach *

This study presents a Geographic Information System (GIS) based system utilizing the Simple Additive Weighting (SAW) method for mapping natural disasters in Situbondo, Indonesia. Within the Ring of Fire along the Pacific tectonic plate boundary, Indonesia is highly prone to various natural disasters, including tsunamis, volcanic eruptions, floods, and earthquakes. However, there is currently a lack of comprehensive disaster mapping and limited availability of online information through the official website of the Situbondo District Regional Disaster Management Agency (BPBD). The proposed system accurately maps the distribution of natural disaster occurrences by analyzing multiple criteria such as topography, proximity to water bodies, population density, land cover, and infrastructure. The system achieved an impressive accuracy rate of 91,8 % by comparing its results with expert evaluations. By leveraging the SAW method, decision-makers can prioritize resources and allocate them to high-risk areas, improving disaster preparedness, response planning, and mitigation strategies. This GIS-based system offers a powerful tool for Situbondo District and BPBD to make informed decisions and proactively manage natural disasters. By enhancing the availability of accurate and up-to- date information, this system supports better coordination and allocation of resources during emergencies, contributing to the overall resilience of Situbondo District and its ability to mitigate the impacts of natural disasters.
Enhancing IoT Image Security Through Hybrid Encryption and Optimal Key Generation with Optimization

In today's world, the Internet of Things (IoT), the essential need for safeguarding digital data, namely digital images and videos, has come to the forefront. This study introduces a novel hybrid encryption technique characterized by specific attributes that favor efficiency and optimum key utilization, enhancing information technology security. Safeguarding the authenticity and privacy of digital images is of utmost importance owing to the many security obstacles presented during their processing and transmission. This research presents the IoT-based Hybrid Image Security Framework (IoT-HISF) using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), which comprises a comprehensive solution to address the difficulties. The research provides valuable contributions by creating high-speed encryption, focusing on generating vital cryptographic keys. The research introduces multi-layered security measures specifically designed to safeguard images in the IoT using the IoT-HISF framework. The IoT-HISF demonstrates performance outcomes in several metrics, such as peak signal-to-noise ratio (PSNR) with a value of 53.15 dB, Hamming distance of 0.02, bit error rate (BER) of 0.0002, accuracy of 97.82%, payload capacity of 5.32 bits per pixel (bpp), and security index of 97.05. These findings emphasize the exceptional image security capabilities of the IoT-HISF framework, particularly in IoT applications. The results indicate that the IoT-HISF enhances image security in IoT applications, including reliable safeguards for high-quality photos and optimizing resource efficiency.