Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preservation of Medical Images Using Hybrid Chaotic Maps

In recent times, the utilization of medical images in telemedicine applications has been experiencing significant growth in healthcare sector. After the COVID-19 pandemic, many online consultations are growing, which is convenient for old age people and time-saving compared to offline consultations. The hospitals send the patient medical reports online. The medical image determines the physical conditions of the patient. There is a chance that intruders misuse medical data for fake insurance claims. Modifying the medical report for intentional purposes undergoes false medical diagnosis. Most security breaches have occurred during the transmission of medical records. The main objective is to use a hybrid chaotic map combining Henon maps and logistic maps to improve the security of medical images. A pseudo-code number generator is utilized for secure key generation to encrypt and decrypt the image using Mersenne Twister. This proposed work has proven to conflict with statistical and differential attacks. The hybrid chaotic maps are implemented using online open-source medical image datasets, getting better results than the existing scheme. Performance of this scheme is measured using various metrics, such as histogram analysis, correlation coefficients, avalanche effect, NPCR, and UACI, to determine its effectiveness. This proposed work provides enhanced security while transmitting medical data in telemedicine applications.
Video Content Analysis Using Deep Learning Models

Videos are abundant online due to the proliferation of inexpensive video recording equipment. However, video content analysis is necessary since most videos cannot be easily classified. Abstract videos, segmentation, and classification are only some video processing issues reviewed here. Examining the potential of kernel techniques, machine learning (ML), and compressive sensing for classification, clustering, reduction of dimensionality, event being identified, and activity identification are all examples of tasks included in video content analysis. Recognition and categorization of video material are investigated using ML. Identification and categorization of video material are investigated using ML. In this research, we look at the strengths and weaknesses of the algorithms in practice. Naive techniques for categorization were utilized. In addition to Bayes and SVM, we also have DCNN equipped with DHO (Deer Hunting Optimization). Compared to the DCNNDHO algorithm, other methods tend to have more excellent rates of false discovery and false alarms.
A Phase Compensation Method for Improving the Stability of Grid-connected Inverter in Weak Grid

With the large-scale application of renewable energy converter equipment in the power grid, the system stability issue becomes serious increasingly. The three-phase voltage-source converter has a wide application prospect in power system, because it can control grid-connected current and inject power quickly and precisely. The gird voltage feedforward is usually used in grid-connected inverter to suppress grid current harmonics and enhance the anti-disturbance ability. Whereas, in weak grid, the grid voltage feedforward will reduce the stability margin. To solve the issue, this paper proposes a phase compensation method which enhances the phase margin of the inverter’s output impedance. The dq-frame impedance model is established and the stability is analyzed by generalized Nyquist stability criterion. The simulation results indicate the validity of the proposed method.
Research on Cardiovascular Disease Classification and Recognition Method Based on Deep Learning

With the continuous increase in the number of patients with cardiovascular diseases worldwide, it is of great significance to effectively and non-invasive identify cardiovascular diseases. Pulse waves contain rich physiological information in the human body and can effectively reflect cardiovascular function. We collected pulse wave data from cardiovascular disease patients aged 30–80 and normal individuals, and trained them using different neural networks based on this data. The results demonstrate that DenseNet had the best prediction with an accuracy of 76.62%.
Comparison of Control Schemes for a GaN-Based Eight-Cell Flying Capacitor Converter DC/DC With Enhanced Loss Model

This paper presents a comparative study of three control methods to control the output voltage of a nine-level Flying Capacitor Converter (FCC) in DC/DC buck applications. The methods considered are: i) Linear controller with Phase Shifted Pulse Width Modulation (PS-PWM), ii) Finite Control Set - Model Predictive Control (FCS-MPC), and iii) Sequential Phase Shifted - Model Predictive Control (SPS-MPC). MPC methods require a detailed model of the system to obtain good results, so a refined loss model is incorporated into the standard FCC equations to minimize output errors, particularly at lower voltage levels and high switching frequencies. This model includes the output inductor and GaN semiconductor's behavior. Simulation results are used to compare the dynamic and steady-state behavior of these three methods showing the excellent performance of SPS-MPC in both cases.
Securing Industrial Control Systems (ICS) Through Attack Modelling and Rule-Based Learning

In recent years, Industrial Control Systems (ICS) have been targeted through a range of cyber-attacks that attempt to infiltrate and disrupt process controls of industrial control systems. This is accomplished through direct manipulation of sensor and actuator states with consequent impact on critical infrastructure such as water treatment and power generation systems. Such cyber-physical attacks can be prevented through rule-based learning techniques. Our work comprises the design of a generator framework that employs the association rule mining technique to automatically generate attack variants and system invariants for an ICS. These generated attack variants and system invariants are validated through experimentation with results showing promise.
Design of an On-Line Monitoring System for Partial Discharge of Gas Insulated Switchgears

An ultrasonic on-line monitoring system for partial discharge of gas insulated switchgears (GISs) was designed based on optical fiber transmission and LabVIEW virtual instrument. The system comprised an ultrasonic wave sensor, remote high-speed acquisition modules, a local modulus and the upper computer software. By utilizing the optical fiber transmission technology, partial discharge signals of GISs were reliably acquired at a high speed. By accurately extracting partial discharge signals, the upper computer software system was developed through LabVIEW graphical programming language. Based on this, this research built the test platform for partial discharge of GISs and simulated four typical types of faults commonly seen in the operation of GISs. By combining with the system and using ultrasonic method, the whole discharge process of different discharge models was detected, thus obtaining the feature database of ultrasonic signals during partial discharge under each defect model. The test research and field application verify the effectiveness of the system and the algorithm proposed in this study.
Automated Leukaemia Prediction and Classification Using Deep Learning Techniques

Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique.
Managing Metadata in Data Warehouse for Data Quality and Data Stewardship in Telecom Industry - A Compact Survey

Present telecom industry database provided poor data quality, specifically metadata quality. As a result, decision making data are missing for analyses. Metadata may contain information about the schema of the data so that the data can be transformed into quarriable data objects when required. The data warehouses (DWH) acceptance has grown hugely for data analysis. The main part of data warehouse includes source of data, software for data acquisition, repository, tools for data analysis, and user interface. So many factors are responsible and be critical for DWH successful implementation, they are type of organization, sufficient technical information, project type, work environment, infrastructure, quality of information, quality of system, quality of Service, and quality of relationship. For data quality and its analysis, metadata, and its roles that metadata plays are very important for organization. Metadata management is a central part of the data analysis process. Metadata carries information about data schema that helps to transformed into quarriable data objects when required. For all projects related to data warehouse, the most important factor is metadata. It catches all types of necessary information to design, simplify, build, and use the data warehouse which contains project related data. Functions of metadata system are to collect and store information in single place, which provides an overview for metadata, such as searches, queries, create, maintain, evaluate and managing access of metadata by establishing role-based security. Stewarding metadata is important for the application of data governance practice for enterprise to provide the value and the context for explaining the data and its components to the end users. With the help of the metadata concepts, the organizations can define a business rule for the specific data attribute.
PCSPred: Prediction of Short Chain Antimicrobial Peptides using Machine Learning Algorithms

Antimicrobial peptides (AMPs) have exhibited an effective and widespread impact in healthcare for evolving into a novel strategy against bacterial infections which have become immune to most of the conventional antibiotics. In-silico approach of evaluating antimicrobial activity for multiple combinations of peptide sequences proves to be optimizational in terms of time and effort when compared to wet lab analysis. In the current study, we have developed a predictive machine learning model which incorporates the physico-chemical and spatial properties of peptide sequences. We propose that the amino acid composition, α-helix and β-sheet propensities, charge-to-hydrophobicity ratio, isoelectric point, and their dipeptide composition are significant features that might be useful parameters for identifying novel AMPs. The PCSPred model utilizes the Random Forest algorithm of predicting, which is capable of providing the best accuracy and precision when compared to other techniques. Additionally, we have developed a means of predicting all possible peptide sequences having a maximum length of 10 amino acids with antimicrobial properties. Thus, we constructed an AMP library containing new sequences which can be implemented in-vivo to synthesize novel AMPs that are successful against resistant microbial infections and would overcome the drawbacks of conventional treatment.
Quasi-Orthogonal Structured Mersenne Matrices for Masking Digital Video and Audio Data in Distributed Systems

The paper considers quasi-orthogonal Mersenne matrices structured according to Walsh. The differences between the system of orthogonal Mersenne-Walsh functions and classical Walsh functions are shown. Two- and four-level (modularly two-level) Mersenne-Walsh matrices are considered. Estimates of their use in image compression and masking and protective encoding of digital audio data are given.
Latency Aware - Resource Planning in Edge Using Fuzzy Logic

As a potential paradigm for enabling effective and low-latency computation at the network's edge, edge computing has recently come into the spotlight. In edge computing environments, resource allocation is essential for ensuring the best possible resource utilization while still satisfying application requirements. Traditional resource allocation algorithms, however, struggle to effectively capture the uncertainties and ambiguity associated with resource availability and application needs because of the dynamic and varied nature of edge environments. This research offers a fuzzy logic-based method for planning to allocate resources in edge computing. Fuzzy logic offers a flexible and understandable framework for modeling and reasoning with imperfect and ambiguous data. The suggested method offers a more reliable and adaptable resource allocation system that can successfully address the uncertainties present in edge computing by utilizing fuzzy logic. The resource allocation process incorporates fuzzy membership functions to capture the vagueness of resource availability and application requirements. Fuzzy rules are defined to map the linguistic variables representing resource availability, application demands, and performance objectives to appropriate resource allocation decisions. The fuzzy inference engine then utilizes these rules to make intelligent decisions regarding resource allocation, considering the fuzzy inputs and the system's predefined objectives.
A Real-Time Phase Analysis System on SSI

A realtime phase analysis system is proposed and deployed for terahertz solid-source interferometer (SSI) on Experimental Advanced Superconducting Tokamak (EAST), which aims at high plasma density measurements and realtime feedback control. The system takes advantages of the simultaneous demodulation correlation algorithm and FPGA hardware. The realtime phase analysis system acquires the signal from the detector of the SSI system, and extracts the phase information via the phase calculation algorithm to calculate the plasma electron density information. The findings show that the system provides realtime, stable and high-precision electron density in EAST control system and it can be used for physical analysis.
Hybrid Modulation Strategy for DC-Bias Current Mitigation in Dual Active Bridge Converters

The use of the dual active bridge (DAB) as an electronic power converter in electric vehicles has gained interest in recent years thanks to features such as bidirectional power flow, galvanic isolation, high efficiency, high power density, low component stress, among others. Phase-shift modulation schemes are widely used for DAB control, being the most basic of them the single phase shift (SPS), followed by modulations with increasing number of degrees of freedom such as the extended phase shift (EPS), dual phase shift (DPS) and triple phase shift (TPS). A relevant concern in DAB applications is the appearance of transient dc components in the transformer current which are generated by load disturbances and the resulting sudden changes in phase-shift. This de-bias current reduces the converter efficiency, induces higher current stress in semiconductor switches and increases the risk of failures due to the saturation of magnetic cores. This paper proposes the implementation of a hybrid modulation strategy which combines different phase shift modulation schemes to mitigate the dc bias of the transformer current under load impacts. The validation of the modulation scheme is done through simulation analysis with the PLECS software.
Research on Optimized Dispatching of the Wind-Solar-Storage Combined System considering the Uncertainty of Wind and Solar

In order to reduce the impact of wind power, photovoltaic and other renewable energy output prediction uncertainty on the economics of day-ahead optimized dispatching operations, a wind-solar-storage joint optimized dispatching method that takes into account the uncertainty of wind and solar energy is proposed. Firstly, based on the generative adversarial networks (GAN) and affinity propagation (AP), the uncertainty characteristics of wind and solar output are converted into a typical set of deterministic scenarios, and based on the wind and solar prediction curves generate wind and solar output typical scenarios. Secondly, with the goal of optimizing the improvement of the net load fluctuation and minimizing the cost of the system operation, a wind-solar-storage joint optimized dispatching model is constructed. Finally, the proposed scheme is simulated and analyzed. The results show that the proposed method can effectively reduce net load fluctuations, improve wind and solar consumption rates, and reduce system operating costs.
Backtracing Social Events of Interest via Logical Correlation Using GDELT

In response to the inability of mainstream methods in existing social event backtracing studies to address the logical correlation problem, the insufficient and inaccurate utilization of event characteristics for implementing logical association backtracing events, we propose a backtracing method for social events of interest. The basic idea is to obtain a set of related events given a small amount of labeled data on interested events, and to construct the causal chain of event occurrence through their merging and fusion. Specifically, we design a node feature construction strategy to select topic and title information from various elements of events to construct node features, thereby addressing the problem of insufficient and inaccurate node feature composition. We also design an adjacency relationship construction strategy, which uses the co-occurrence of participants in event records as the basis for constructing adjacency relationships, and completes the final construction of adjacency relationships by considering time elements comprehensively, thereby resolving the problem of inaccurate construction of inter-event correlation relationships. We construct a multi-layer graph convolutional network to learn the associated features and achieve node classification through a normalization function, thereby solving the problem of logical association inference between events. Experimental results demonstrate that LC-BTE is effective in backtracing analysis of events related to social events of interest. Additionally, this paper explores the performance changes of the method under semi-supervised mode and conducts experimental analysis. Finally, this paper provides the causal chain of events based on the backtracing results.
Outperforming Cryptocurrency Price Prediction Using Deep Reinforcement Learning Approach

Cryptocurrency price prediction is a complex and dynamic task. Cryptocurrencies are highly volatile digital assets impacted by a wide range of variables, including investor mood, legislative changes, market demand, and technical breakthroughs. This research presents a novel approach to cryptocurrency price prediction using Deep Reinforcement Learning (DRL). The proposed model incorporates proximal policy optimization (PPO) and a Hybrid Convolutional Neural Network Long Short-Term Memory(CNN-LSTM)model. The proposed model was evaluated for three benchmark datasets such as Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). This novel approach significantly improves the prediction rate by investigating price movements and optimizing investment returns. The experimental results show the anticipated daily and weekly returns and the actual and forecasted prices using a sentiment analysis approach. The proposed model outperforms existing approaches by obtaining profits scores such as 1.04 for the LTC dataset, 0.98 ETH dataset, and 0.97 BTC dataset. These scores justify the effectiveness of the proposed model by generating favorable returns in cryptocurrency investments.
Accessory Based Classification of Football Players Using Attention UNet

Accurate soccer player classification is essential in the realm of sports video analytics, allowing for comprehensive evaluations of player performance and potential. In this study, we propose the adoption of an Attention UNet model for precise player categorization based on their distinct accessories. The innovative Attention UNet architecture, equipped with attention gates, excels at capturing and enhancing pertinent features, ensuring highly accurate classification. We conducted evaluations using a carefully curated custom dataset comprising 500 images depicting goalkeepers, referees, and captains. Performance metrics including Mean Squared Error (MSE) loss, Intersection over Union (IoU), dice coefficient, precision, and recall were rigorously applied. In stark contrast to existing soccer player classification methods, our approach demonstrates substantial performance enhancements, achieving an impressive accuracy rate of 98.93%. This research fills a critical need for precise player classification, offering invaluable insights for player tracking, performance analysis, and data-driven decision-making in the domain of soccer analytics. By providing this innovative framework, we aim to elevate the capabilities of sports video analytics, benefiting researchers, analysts, and sports enthusiasts who look for advanced tools for player assessment and talent identification.
An Automatic Threshold Adjustment Algorithm for Thermal Fault Extraction

Infrared thermal (IT) image plays an important role in thermal fault detection for monitoring electric power equipment, because IT image can detect temperature difference information between normal and faulty power equipment. There are some problems with traditional thermal fault extraction methods, such as the low degree of automation and the inaccuracy of the extracted thermal fault area caused by the excessive detection range. To address these challenges, this paper proposes an automatic threshold adjustment algorithm for thermal fault extraction. Firstly, the IT image is converted to grayscale. Then, the statistical analysis of the grayscale histogram is performed. Based on the distribution of grayscale values, the quantity of grayscale differences defined in this paper is extracted at the boundary between the bright regions around the thermal fault area and the dark regions in the background. Subsequently, the peak of local minimal value is identified as the optimal threshold. Finally, the grayscale image is binarized using this optimal threshold, facilitating the extraction of thermal fault area. The algorithm was tested on more than 20 thermal fault images of power equipment, which shows that the algorithm can accurately extract the fault area.
Unveiling the Power of Transfer Learning: A Comparative Analysis of Video Features in Captioning Task

This research presents a comprehensive comparative analysis of transfer learning features in the domain of video captioning, focusing on the extraction of features from prominent pre-trained models, including VGGNet-16, VGGNet-19, ResNet-50, ResNet-101, DenseNet-121, Inception-V3, and MobileNet-V2. The investigation emphasizes the significance of transfer learning for capturing temporal dynamics in videos and highlights the effectiveness of motion vector features in relation to object type and movement. Among the considered models, ResNet-101 emerges as the most impactful, showcasing superior object-oriented feature extraction capabilities. The study not only underscores the importance of model selection in video captioning tasks but also provides valuable insights into the nuanced performance of diverse pre-trained models. These findings contribute to the understanding of transfer learning's role in video analysis, offering guidance for practitioners and researchers seeking optimal solutions for video captioning applications.