Reliable Mechanism Research Articles

Multi-view fair-augmentation contrastive graph clustering with reliable pseudo-labels

Graph contrastive clustering (GCC) has achieved numerous advantageous results due to the information mining capability of self-supervised learning. Multi-view attribute graph clustering, as a means of addressing complex attribute graph data with multi-view relations or features, has gained significant attention in recent years. However, only a few GCC methods are applicable to multi-view graph data, limiting the full utilization of the rich information contained in such data. Furthermore, most GCC methods rely on random augmentation strategies, leading to structural unfairness in the augmentation process across views. To address these issues, we propose multi-view fair-augmentation contrastive graph clustering with reliable pseudo-labels (MFCGC), which combines contrastive learning and fully utilizes the node-level and cluster-level information within each view of the multi-view data, ensuring the consistency of information across different views. Moreover, we designed a carefully crafted node-degree-based augmentation method called fair augmentation, which preserves the partial topology structure of multi-view data. Finally, we propose a reliable pseudo-label selection mechanism that integrates reliable pseudo-labels from multi-view graph to improve the quality of sample pairs. Our proposed MFCGC has been extensively evaluated on real-world datasets, demonstrating its superiority over state-of-the-art algorithms in two categories of multi-view graph data and attesting to its significant effectiveness.

Recognition mechanism of split T-2 toxin aptamer coupled with reliable dual-mode detection in peanut and beer

T-2 toxin is a highly poisonous trichothecene and a well-known contaminant in cereal grains and cereal-based products, hence it is essential to design a simple, reliable, and precise detection mechanism for ensuring food safety and human health. In response to this imperative, we innovatively designed an unreported split T-2 toxin aptamer probe, and the cooperative binding mechanism of the split aptamer to T-2 was first investigated. Based on these, a dual-mode fluorescence (FL) and fluorescence polarization (FP) aptasensor was developed for T-2 toxin detection in peanut and beer samples. The results showed an excellent detection property of the aptasensor, indicating a linear range of 0.1–20 nM and 0.2–50 nM at a detection limit of 0.1 nM, 0.12 nM for FL and FP signals, respectively. In actual sample assays, recoveries of 101.7%–115.2%, 109.8%–121.6% for FL and FP mode, respectively, were reached in peanuts, whereas recoveries ranged from 81.0% -124.0%, 94.1%–107.8% for FL and FP mode in beer. This designed dual-mode biosensor not only showed excellent performances but also verified each other and proved the reliability of binary split aptamer molecular probes.

RRefined and Effective Integration of Access Control Mechanism in Blockchain Enabled DApp

The combination of the Internet of Things (IoT) and aviation integration with the help of satellite and 6G communication technologies has led to the Internet of Unmanned Aerial Vehicles (UAV) or Internet of Drones (IoD). To host and share large amounts of drone data in real time, cloud-based IoD algorithm Cloudbased IoD is the wrong choice to reduce the weight of mobile drones. However, how to protect the sensitive drone at the fair is a matter of curiosity, and it is very difficult to break the environment where drone use is available and limited resources are available. Although our previous work in SPNCE21 (PATLDAC) developed an air-on middle of the main load of based drone data access control system with privacy policy, limited access time and user tracking, it provides conflicting and centralized cloud data storage for cloud environment and access with unreliable data. It is not reliable in terms of data access and tracking of users. To this end, we propose a blockchainbased privacy data access control (BPADAC) scheme for distributed and secure drone data sharing on cloudbased IoDs. Building on the granular, traceable, and privacypreserving drone data access capabilities of our previous work, we extend this by using blockchain and decentralized hash tables (DHT) to provide secure and reliable wireless access to humanmachine data and storage. as well as reliable and limited access guarantee mechanisms for cloud drone data sharing services. We also set up a public and denial user tracking system to prevent user abuse and deny the traitor. Finally, we propose a prototype for security analysis and performance evaluation using smart contracts on the Ethereum blockchain to demonstrate the feasibility of BPADAC. hash table, attributebased ciphertext encryption policy, and blockchain-based privacy-protected enabled and aware data access control Key Words: Blockchain, UAV,IoD, Distributed Hash Table, Ciphertext-Policy-Attribute-Based-Encryption, blockchainbased privacy-aware data access control

Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare

The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis.

Securing File Storage by Hybrid Cryptography

The proliferation of digital data in modern times necessitates robust security measures to safeguard confidentiality and integrity. This paper introduces a "File Storage System using Hybrid Cryptography," a pivotal solution addressing the pressing need for secure file storage and retrieval. Hybrid cryptography merges symmetric and asymmetric encryption techniques, ensuring a resilient and reliable data protection mechanism. This system leverages the strengths of symmetric encryption's speed and efficiency while integrating the key management advantages of asymmetric encryption. Through an exploration of historical cryptographic foundations, the emergence of hybrid cryptography, and contemporary file storage challenges, this paper highlights the importance of such an approach. The proposed system's phases, from encryption to decryption, elucidate its implementation and efficacy. Future research directions are outlined, focusing on enhanced key management, performance optimization, multi-platform integration, and user-centric solutions. Challenges such as quantum computing threats, scalability, and integration with emerging technologies are discussed. The conclusion emphasizes the significance of hybrid cryptography in addressing data security concerns amidst escalating digital data volumes, offering an efficient and secure framework for data storage and sharing. The research gaps identified prompt further exploration into quantum-resistant systems, scalability optimization, user-friendly interfaces, cross-cultural legal considerations, ethical implications, sustainability, and integration with emerging technologies.

Artificial intelligence (AI) inspired an innovative approach for real-time flow measurements – water distributions at outlets, in Punjab province Pakistan

ABSTRACT Accurate water metering is essential for effective water management, especially for measured and equitable distribution of canal flows, most imperative in Pakistan. The inflows distributed through outlets must adhere to pre-designated levels, defined by Punjab Irrigation Department from head to tail. With recent channels lining, the outlets sanctioned flow are found changed requiring adjustments. Furthermore, there is lack of reliable mechanisms for continuous flow regulations, resulting in unmanaged distributions. The focus of the reported work was on precise and real-time discharge monitoring at outlets using a newly developed device utilizing Artificial Intelligence. During validation training, strong agreement was observed, and all evaluation parameters supported the device accuracy for measurements. In detailed studies of canal operations, the sanctioned discharges of individual outlets along selected distributaries were found significantly inequitable. Out of 162 outlets, 42 were suspended for being unmanaged, with tail users receiving ≤ 50% of their fair intakes. Additionally, there were instances of massive irrational supplies exceeding 200% of allocated discharges. The developed device has potential to accurately measure real-time discharges for managed distributions. Conclusively, the challenging task of fair and equity-based discharge allocations, in conjunction with Warabandi system, necessitates the reevaluation of the allocated flow quantity at each outlet.

Reliability oriented key quality characteristics driven integrated built-in reliability activity chain and approach for manufacturing process

Product reliability is determined at the design phase and built into the product through manufacturing process. To effectively implement reliability activities at each stage, reliability data in product life cycle is integrated with KQC (key quality characteristics), and the concept of R-KQC (reliability-oriented KQC) is proposed. Based on the R-KQCs, this study presented an integrated Built-in reliability (BIR) approach to ensure that the product reliability formed losslessly conforms to the reliability goal determined in the design phase. First, the connotations of BIR and BIR-oriented RQR (manufacturing system Reliability, manufacturing process Quality, and product Reliability) chain are proposed by considering the evolution mechanism of product reliability in the life cycle driven by the R-KQCs. Second, a product BIR activity chain consisting of plan, synthesis, and improvement based on R-KQC is presented by the BIR-oriented RQR chain. Third, an R-KQC-driven BIR assurance technology framework based on the BIR activity chain is proposed, and a product reliability modeling considering the deviation of R-KQCs is established based on the framework. Finally, the validity of the proposed approach is verified using the reliability assurance for an automobile engine cylinder head as an example.

Distributed realtime rendering in decentralized network for mobile web augmented reality

Low-latency, real-time rendering of 3D objects is critical for mobile web-based augmented reality (MWAR) applications. While cloud-based or edge-based server rendering offloading can reduce the latency for mobile devices, a high volume of user requests in user aggregation scenarios can overload servers and transmission channels. It can result in a poor user experience due to resource consumption and high latency. This paper presents a decentralized and collaborative real-time rendering offloading network architecture (CRCDnet) to address this issue. CRCDnet makes contributions in the following three areas: (1) In the user aggregation scenarios, mobile devices that perform the same services are used as service nodes to perform the offloading of rendering computing and a collaborative rendering computing network is established. (2) For data exchange in decentralized networks, a data sharing middle layer based on blockchain key indexes separates sensitive service data and ensures a secure, reliable exchange mechanism and efficient data exchange. (3) A data request and computing offloading scheduling approach is proposed for the collaborative rendering computing network to optimize the rendering computation delay.

Water vapor content prediction based on neural network model selection and optimal fusion

Accurately predicting the evolution of water vapor content has significant practical engineering implications for accurately determining the timing of artificial rain enhancement operations. However, the nonlinear, unstable, and chaotic nature of water vapor content presents considerable challenges to its precise prediction. To address this, our research proposes a combined prediction model, based on a neural network model selection mechanism and small sample water vapor content data observed by microwave radiometers in the Zhengzhou area. This combined model utilizes seven neural network models as candidate models and dynamically selects the optimal three models as the central prediction components via evaluation indicators. Additionally, we employ the maximum information coefficient method to preserve the optimal input features and use a time-varying rate wave mode decomposition algorithm as the front-end processing module. This module processes the original water vapor content sequence and extracts relevant latent information. Finally, we incorporate the African vulture intelligent optimization algorithm as the backend processing component to optimize the weighted combination coefficients, achieving the integration of prediction results. Experimental results reveal that the Mean Absolute Percentage Errors (MAPE) of the proposed model on three types of water vapor content datasets are 0.208%, 0.876%, and 0.369%, respectively. This suggests a predictive performance improvement of at least 10% compared to existing models. The prediction model proposed in this study not only establishes an accurate and reliable mechanism for predicting water vapor content but also offers more comprehensive decision support information for weather modification operations.

Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection

One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently.

Seismic Isolation of Fragile Pole-Type Structures by Rocking with Base Restraints

Pole-type structures are vulnerable to earthquake events due to their slender shapes, particularly porcelain cylindrical equipment in electrical substations, which has inherent fragility and low strength in its materials. Traditional base isolation designs configure the bottom of the pole-type equipment as hinges with restraints. It fully relies on the restrainers to re-center the pole-type equipment, posing a risk of tilting and functionality failure after earthquakes. This study proposes a solution to this challenge by introducing a restrained rocking mechanism at the base of the structure. The design leverages the self-centering nature of rocking motion and uses restrainers to control the amplitude of rotation. Hence, it can effectively avoid tilting of the pole-type structures after earthquakes. Experimental investigations conducted on a 1:1 full-scale specimen revealed that the proposed restrained rocking design can achieve a reduction in seismic internal forces of over 50% while maintaining equipment in an upright position. Furthermore, an analytical model for the proposed isolation system of pole structures was developed and validated through comparison with experimental results. This paper introduces a novel solution for seismic isolation of pole-type structures through restrained rocking, specifically addressing the research gap regarding a reliable self-centering mechanism under seismic excitation. This advancement significantly enhances the seismic resilience of fragile pole-type structures and provides practical design methodologies for the seismic isolation of slender structures.

Deep Self-Supervised Attributed Graph Clustering for Social Network Analysis

Deep graph clustering is an unsupervised learning task that divides nodes in a graph into disjoint regions with the help of graph auto-encoders. Currently, such methods have several problems, as follows. (1) The deep graph clustering method does not effectively utilize the generated pseudo-labels, resulting in sub-optimal model training results. (2) Each cluster has a different confidence level, which affects the reliability of the pseudo-label. To address these problems, we propose a Deep Self-supervised Attribute Graph Clustering model (DSAGC) to fully leverage the information of the data itself. We divide the proposed model into two parts: an upstream model and a downstream model. In the upstream model, we use the pseudo-label information generated by spectral clustering to form a new high-confidence distribution with which to optimize the model for a higher performance. We also propose a new reliable sample selection mechanism to obtain more reliable samples for downstream tasks. In the downstream model, we only use the reliable samples and the pseudo-label for the semi-supervised classification task without the true label. We compare the proposed method with 17 related methods on four publicly available citation network datasets, and the proposed method generally outperforms most existing methods in three performance metrics. By conducting a large number of ablative experiments, we validate the effectiveness of the proposed method.

Sensitive and Obscene Content Blocker (SOCB)

Abstract: In today’s digital age, the internet is a vast and diverse platform that offers a wealth of information and entertainment. However, this openness also exposes users to sensitive and obscene content, which can have a detrimental impact on individuals, particularly children and vulnerable populations. To mitigate the exposure to such content, a Sensitive and Obscene Content Blocker has been developed and tested. This paper explores the design, development, and evaluation of a comprehensive content filtering system that is aimed at identifying and blocking sensitive and obscene content across various online platforms. The system employs advanced algorithms, machine learning techniques, and real-time content analysis to ensure a reliable and robust filtering mechanism. The paper outlines the key components of the blocker, including content analysis methods, their detection and classification methods. The study presents the results of extensive testing, demonstrating the system’s efficiency in blocking inappropriate content while minimizing false positives. The Sensitive and Obscene Content Blocker serves as a powerful tool to protect individuals from harmful online content, promoting a safer and more secure digital environment. This paper provides valuable insights into the development and implementation of content blockers and their role in enhancing online safety and security.

Fake Product Identification System for E-Commerce Applications

Abstract: The use of blockchain technology, combined with QR codes, presents a promising solution for identifying and combating counterfeit products efficiently and securely. By integrating a blockchain-based system with QR code scanning, stakeholders can establish a reliable mechanism to verify product authenticity. This system involves storing product details and generating unique QR codes for each product as blocks in the blockchain database. When a QR code is uploaded, the system compares the code against entries in the blockchain database. If a match is found, the customer receives a notification confirming the product's authenticity; otherwise, they are alerted that the product is fake. Blockchain technology's ability to eliminate the need for trusted intermediaries, facilitate faster transactions, and provide transparency makes it particularly intriguing for addressing counterfeit issues. However, reducing counterfeits requires a comprehensive approach, including increasing awareness, legal action against counterfeiters, implementing a robust alert system, and ensuring secure packaging. These factors, combined with blockchain technology and digital signature verification, can lead to a more efficient and thorough strategy for combating counterfeiting in the marketplace

Comparative review of advanced seatbelt detection: Infrared sensors vs. image-based systems

The seatbelt, often heralded as the first line of defense in vehicular safety, plays an indispensable role in mitigating the risks associated with traffic incidents. Consequently, the push towards enhancing transportation safety has led to the emergence of sophisticated seatbelt detection systems. In this study, we undertake a comprehensive examination of four cutting-edge detection methodologies: RF (Radio Frequency), Infrared Marker Vision, Convolutional Neural Networks (CNN), and the You Only Look Once (YOLO) approach. Each method is dissected to understand its efficacy in determining if a passenger has properly donned their seatbelt. Beyond just immediate detection, this paper also casts a vision towards the horizon of seatbelt detection advancements. We explore how such detection systems might seamlessly integrate with advanced vehicle safety infrastructures, and postulate on their pivotal role in the burgeoning domain of autonomous vehicles. As self-driving cars become an imminent reality, the importance of reliable seatbelt detection mechanisms will only magnify. Thus, through this paper, we not only shed light on current methodologies but also endeavor to chart the trajectory of future innovations in the realm of seatbelt safety detection.

Evaluating the Performance of Foresight Studies: Evidence from the Egyptian Energy Sector

Foresight projects are expected to provide realistic scenarios for different future scenarios, which provides a better information base for relevant strategies. However, these expectations often turn out to be at least difficult to fulfill due to the uncertainty of the external environment and cognitive biases. Therefore, the idea of assessing each stage of Foresight is gaining relevance, which is of particular importance in the energy sector, which affects a variety of areas of life. This article analyzes the results of the Egyptian energy foresight study, Egypt LEAPS, in terms of process efficiency and forecast accuracy as well as the factors that influenced it, including cognitive biases. The authors conclude that for each stage of foresight, a thorough analysis of weaknesses and shortcomings is necessary. Therefore, from the very beginning, the foresight process should include reliable mechanisms for assessing results and a readiness for constant iterations. Consistent process adjustments that rely on new ways of dealing with complexity and uncertainty in dealing with the future help build trust among participants and consistently reduce the level of erroneous assumptions.

Effects of high temperature and high humidity on the reliability of copper/epoxy bond

Epoxy encapsulation is a promising technique to reduce the size of the power module. Its reliability is a key aspect. Many previous studies developed new epoxy materials or new surface treatment methods to improve reliability. However, reliability mechanisms are still not well understood. This study focused on the reliability of copper/epoxy bonds under high temperature and high humidity conditions because previous studies did not reach a unified conclusion on this issue. The copper/epoxy bonds are fabricated by compression molding. Then, these copper/epoxy bonds are placed in the 85 ℃/85 %RH reliability test for 1000 h. The bonding strength change was measured. Surprisingly, the bonding strength between copper and epoxy increased after the 85 ℃/85 %RH reliability test. The possible reason was investigated by observing the fracture surface and bonding interface in a nanoscale. In addition, molecular dynamics simulations were performed to better understand the experimental results. The results showed that the water absorbed in the epoxy caused complex physical and chemical changes at the copper/epoxy interface. The formation of a new cuprous oxidation layer with aligned crystal orientation is supposed to be the reason for the bonding strength increase. Our findings will provide a better understanding of copper/epoxy bonds.

Принципи правового регулювання інституту державної служби

The article analyzes the principles of legal regulation of the civil service institute. The ontological, axiological and praxeological value of the civil service institution, its actualization in the context of Ukraine's European integration efforts, has been proven. It is indicated that today this institution becomes not only an internal mechanism of legitimate, democratic control over the effective exercise of power, but also achieves the necessary functions of transformation of the entire state-power apparatus by improving the power system and bringing it closer to European management standards. The basic principles of legal regulation of the civil service are defined as key standards for the normalization of this institution's functioning of the power system, in particular: legality and legal obligation, which provides that the institution's functioning should be based on clear normative acts and relevant legal norms; transparency and justice, indicates that the process of making state-authority decisions should be open and transparent, include guaranteed opportunities to participate in the process, providing a guarantee for presenting one's position, as well as ensuring the right to defense and the possibility of appealing the decision; confidentiality and protection of personal data, which consists in the fact that all data related to public service must be protected from unauthorized access and use; taking into account the specifics of the activity, it is assumed that when implementing legal and organizational-management measures, one should take into account the specifics of work and foreign-political and internal factors of labor relations; the principle of responsibility of civil servants to society. By creating a complex set of basic principles, they form a matrix of legal regulation of the civil service institution, contribute to an effective and fair rule-making and law enforcement (law enforcement) process in the researched area. The principles guarantee the provision of high standards of behavior of employees and provide a reliable mechanism for monitoring compliance with legal and ethical norms, form an institutional and organizational mechanism for the efficiency and independence of the power system, and are factors in increasing public trust in the decisions of authorities and improving the quality of public administration.

Abstract 2461: Discovery of an orally available and liver-targeted ALPK1 agonist for cancer immunotherapy

Abstract Boosting anti-tumor immune responses can be achieved by activating the innate immune system. Despite the ongoing development of numerous STING and TLR agonists for this purpose, none of them have progressed to late-stage clinical evaluation. Alpha protein kinase-1 (ALPK1) is an intracellular innate immune receptor newly identified for its ability to detect ADP-heptose, a metabolite released during the biosynthesis and degradation of lipopolysaccharides (LPS). Since heptose is not naturally present in mammalian cells, the recognition of heptose metabolites by ALPK1 serves as a reliable mechanism to identify bacterial infections. When ADP-heptose binds to ALPK1, it initiates the phosphorylation of TIFA, thereby triggering a cascade of immune responses. Systemic administration with ADP-heptose induces robust ALPK1-dependent cytokine responses in mice. While high doses of ADP-heptose demonstrate anti-tumor effects, its instability and low in vivo potency hinder its clinical development. Through a SBDD medicinal chemistry effort, a novel, stable, and potent ADP-heptose analog, designated PTT-936, has been discovered and is progressing into clinical evaluation in cancer patients. PTT-936 exhibits remarkable nanomolar potencies for ALPK1 activation in various cellular assays. Notably, in human PBMCs, PTT-936 induces both concentration-dependent release of cytokines and activation of NK and T cells. Consistent with in vitro studies, oral administration of PTT-936 triggered significant cytokine responses in mice at microgram dose levels. However, overt toxicities were observed only at milligram dose levels, indicating a wide therapeutic window for this compound. In syngeneic tumor models, oral administration of PTT-936 induced dose-dependent tumor growth inhibition, which correlated with elevations in several cytokine biomarkers. Mechanistic studies show that PTT-936, working exclusively through the ALPK1 pathway, elicited strong innate and adaptive immune responses, as evidenced by robust cytokine induction and immune cell activation and recruitment. In syngeneic mouse models, the anti-tumor effects of PTT-936 were significantly hindered by either CD8+ T cell depletion or neutralization of select cytokines. Besides monotherapy studies, compelling evidence demonstrates strong synergistic anti-tumor effects when combining PTT-936 with immune checkpoint inhibitors. Intriguingly, PTT-936 exhibits favorable tolerability and a wider therapeutic index compared to certain clinical-stage STING and TLR agonists. Furthermore, PTT-936 does not enhance cytokine elevations induced by checkpoint inhibitors, which have been linked to adverse events (including CRS) observed in clinical combination studies. The dosing frequency of PTT-936 in the upcoming first-in-human clinical trial is carefully designed to prioritize both tolerability and patient compliance. Citation Format: Yupeng Wang, Nana Du, Xiaona Dong, Xuebing Sun, Yuan Hong, Xiaolong Yang, Zhijiang Ji, Qianyu Wang, Yun Lv, Zhichen Shi, Yajuan Gao, Xiaofu Hou, Yaxin Zhang, Juan Wang, Rui Mao, Yinxin Liu, Liang Li, Jingjin Ding, Hexiang Wang, Nan Hu, Counde O’Yang, Zhihong Li, Keith Bley, Feng Shao. Discovery of an orally available and liver-targeted ALPK1 agonist for cancer immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2461.

Credit Card Fraud Detection By IJISRt

While credit card fraud and abuse are the way becoming more common, the convenience using of there credit for the way online purchases has also improved. These fraudulent activities pose a severe financial danger to both credit the are card users and way they using it financial institutions. The first thinking aims to do of this research project is to recognize and put an end to these kinds of fraudulent activities. It addresses a broad range of subjects, including the frequency of false positives, imbalanced datasets, evolving fraud trends, and restricted public data access. The literature now under publication offers a range of machine learning-based methods, including logistic regression, decision trees, random the forests, support to the vector of the +machines, and XG Boost, with the purpose of identifying credit card fraud. However, these methods often exhibit lower accuracy rates, highlighting the need for more advanced deep learning algorithms in order to effectively lower fraud losses. Therefore, the of way they primary thinks objective of this is to improve fraud detection abilities through which the way this application most be of state- of-the-art deep learning algorithms. An assessment way it should be of the research project reveals better outcomes, including optimized AUC curves, precision, f1-score, and accuracy. The ultimate objective there are is to create models that will greatly improve credit card fraud detection and prevention. This research focuses on advanced deep learning techniques in to the way it is order they provide the to more reliable and accurate fraud detection mechanisms, enhance security for credit card users, and lower financial risks for financial institutions when conducting online transactions.