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Audit Risk Identification based on Mutual Information with Backpropagation Neural Network 1

Audit Risk Identification based on Mutual Information with B...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Su, Di Tianjin Tianshi College Tianjin China

ISBN: (纸本)9798331529246

In recent years, identifying audit risk had become important for maintaining financial transparency and detecting fraud. Instead of focusing solely on risk valuation, there had been an increased demand for advanced audit systems. The existing methods suffer from irrelevant feature inclusion, slow convergence in audit risk classification. Therefore, this research proposes Mutual Information based Backpropagation Neural Network (MI-BPNN) for identifying audits risks. Initially, audit data dataset is taken which incorporated audit data information of various firms. The collected audit is preprocessed by utilizing data cleaning which removes errors and normalization for ensuring uniform. Then this audit data features are selected by employed MI method for selecting the most informative indicators. The selected relevant features are evaluated for detecting the fraudulent firms by utilizing BPNN method. The proposed MI-BPNN achieved better results which comprises accuracy of (98.58%), specificity of (97.95%), sensitivity of (96.69%), and recall of (97.20%) when compared with existing Long Short-Term Memory (LSTM). © 2025 ieee.

关键词： audit risk identification backpropagation neural network data cleaning fraudulentfirms mutual information

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Streamlining data Exchange in intelligent Mines: Cross-System Services Leveraging the Industrial Internet Platform 9

Streamlining Data Exchange in Intelligent Mines: Cross-Syste...

引用

9th international conference on Cloud computing and Big data Analytics, ICCCBDA 2024

作者： Wan, Maoquan Wang, Hao Mine Big Data Research Institute Chinese Institute of Coal Science Beijing China

ISBN: (纸本)9798350373554

In the dynamic landscape of mining production, the integration of intelligent systems has revolutionized operations across various domains. This paper addresses the challenges encountered during the construction of intelligent mines, emphasizing disparities in interconnection, data sharing, and protocol standardization. Leveraging the capabilities of Industrial Internet Platforms, we propose a cross-system unified data service tailored for the mining industry. The data model encompasses diverse operations, providing a structured framework for efficient cross-system data exchange. The platform architecture ensures standardized data handling, security, and the provision of interfaces. A low-code configuration tool facilitates plug-and-play functionality, thereby optimizing the efficiency of data interactions. Additionally, microservice containers enhance deployment flexibility. The implementation in an underground coal mine demonstrates significant improvements in data efficiency, security, and integration. The proposed approach offers a comprehensive solution to the challenges of non-standardized and error-prone data interaction within mining subsystems. © 2024 ieee.

关键词： data integration

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 ieee.

关键词： Adversarial machine learning

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Energy-Efficient Computation Offloading with Adaptive Modulation Index and Deadline-Aware Task Allocation in Multi-Access Edge computing 17

Energy-Efficient Computation Offloading with Adaptive Modula...

引用

17th ieee international conference on advanced Networks and Telecommunications systems, ANTS 2023

作者： Chouhan, Sonali Centre for Intelligent Cyber Physical Systems Center for Disaster Management and Research Indian Institute of Technology Guwahati Department of Electronics and Electrical Engineering Guwahati India

ISBN: (纸本)9798350307672

Multi-access edge computing (MEC) has emerged as a pivotal paradigm to enhance the efficiency of computation offloading by placing computing resources closer to data sources. In this paper, we tackle the challenge of energy consumption and latency in MEC environments while maintaining task deadlines and ensuring reliable communication. We present an integrated optimization approach that dynamically adjusts the modulation index and offloading decisions to minimize energy consumption per bit. Our approach considers task deadlines, application bit error rate (BER) constraints, and system limitations. We propose a distributed offloading decision algorithm that categorizes tasks into strict and loose deadline tasks, optimizing resource allocation between the mobile device and edge server. Furthermore, we employ an earliest deadline first (EDF) scheduling policy for efficient task scheduling. Through extensive simulations, we demonstrate that in certain scenarios, our method achieves significant energy savings of 53.5% while upholding BER requirements and reducing communication delays. © 2023 ieee.

关键词： Computation offloading

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A Methodology for Forest Fire Detection and Notification Using AI and IoT Approaches 2

A Methodology for Forest Fire Detection and Notification Usi...

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2nd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Ravi, Nikhil Narayan, Nikhil D'Souza, Prathik Joel New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This study explores how advanced technology has revolutionized the use of drones for forest fire surveillance. This research study explores the historical development of Unmanned Aerial Vehicles (UAVs) in forest monitoring with a focus on control systems and detection algorithms. The drone's capabilities are improved via telemetry integration, sophisticated autopilot systems, and an emphasis on accuracy, autonomy, and real-Time communication. An analysis of object recognition, anomaly detection, and environmental monitoring algorithms in detail demonstrates how important a role these algorithms play in spotting dangers like forest fire. This study presents successful implementations through illustrative case studies, discusses obstacles, and lays forth future plans for utilizing drone technology for the forest fire surveillance in environmental conservation initiatives. © 2024 ieee.

关键词： Autonomy Control systems Cutting-edge technology Detection algorithms Forest surveillance drones Historical evolution Real-Time communication Unmanned Aerial Vehicles

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Improving the search for information from Kazakh-language content in search systems

Improving the search for information from Kazakh-language co...

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2024 ieee international conference on Modeling, Simulation and intelligent computing, MoSICom 2024

作者： Alibiyeva, Zhibek Mukazhanov, Nurzhan Akhmediyarova, Ainur Oralbekova, Dina Kassymova, Dinara Satbayev University Department of Software Engineering Almaty Kazakhstan Information Processing and Storage Satbayev University Department of Cybersecurity Almaty Kazakhstan Institute of Information and Computational Technologies of the Committee of Science Ministry of Science and Higher Education of the Republic of Kazakhstan Almaty Kazakhstan Mukhametzhan Tynyshbayev Alt University Department of Information and Communication Technologies Almaty Kazakhstan

ISBN: (纸本)9798331533311

The presented paper is devoted to exploring the ways to enhance the possibility of finding Kazakh-language text in search engines and improve the quality of search results. There have not been many studies conducted on data retrieval in the Kazakh language, and major search engines do not prioritize processing languages with small audiences, such as Kazakh. During the study, an analysis was conducted on the search results for Kazakh-language data in two search engines frequently used in Kazakhstan: *** and ***. The analysis revealed that these search engines are unable to sufficiently process textual data in Kazakh. To address this issue, the study proposed utilizing morphological analysis and identifying basic forms of searching words. Further, a test search engine was developed taking into account the identified shortcomings, and its results were compared with the results of other search engines. © 2024 ieee.

关键词： Search engines

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 ieee.

关键词： Smart contract

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Hypergraph-based Modeling of Cognitive dataflow systems 15

Hypergraph-based Modeling of Cognitive Dataflow Systems

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15th ieee international conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Hajdu, Csaba Csapo, Adam B. Department of Informatics Széchenyi István University Győr Hungary Institute of Data Analytics and Information Systems Corvinus Institute for Advanced Studies Corvinus University of Budapest Budapest Hungary

ISBN: (纸本)9798350378245

Cognitive systems have become widespread in various computer-based applications, from assistants to robotic systems. Although such systems are useful even in their current version, there is a growing need to incorporate semantic information. This paper introduces HyMeKo, a framework for modeling information using hypergraphs, which allows for the creation of multiple relationships between information entities through a LALR(1)-based language. The framework’s primary application contexts are in robotics, cyber-physical systems, and intelligent systems. A key innovation of this work is a transformation method between hypergraph semantic descriptions and e-adjacency tensors. An additional practical result from the implementation of the framework is a hypergraph-based dataflow system, enabling the simultaneous description and execution of dataflow processes. The paper includes a minimal example to compare the compactness of the introduced language with the widespread URDF, to describe a minimal, 2-link system, demonstrating its advantages in terms of conciseness and flexibility. © 2024 ieee.

关键词： intelligent systems

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IoT-Driven Enhancement of Hydroponic Fertilization Efficiency Through Machine Learning: A data-Centric Strategy 2

IoT-Driven Enhancement of Hydroponic Fertilization Efficienc...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Patel, Juhi Bhatt, Tejaskumar Joshi, Aditi GLS Univ Fac Comp Applicat & Informat Technol Ahmadabad Gujarat India GLS Univ Fac Engn & Technol Ahmadabad Gujarat India

ISBN: (纸本)9798331540661;9798331540678

Hydroponic systems offer a controlled environment for plant growth, where precise nutrient management is crucial for maximizing yields. This study integrates IoT sensors with machine learning algorithms to optimize nutrient composition-focusing on Nitrogen (N), Phosphorus (P), and Potassium (K)-in hydroponic setups. By leveraging data collected from IoT sensors, the machine learning models predict optimal nutrient levels, thereby improving nutrient use efficiency, plant growth rate, and overall yield. The results show that the IoT-enabled precision fertilization system increased nutrient use efficiency by 25%, enhanced plant growth rate by 20%, and boosted yield by 15% compared to traditional nutrient management methods. Additionally, the system's ability to provide real-time adjustments and remote monitoring minimizes human intervention and reduces nutrient wastage. This research highlights the transformative potential of data-driven approaches in controlled-environment agriculture, offering a sustainable path to significantly enhancing productivity. Future work will focus on expanding datasets and incorporating more advanced IoT sensors and machine learning algorithms to further refine the system's accuracy and adaptability.

关键词： Hydroponics IoT sensors Machine learning Nutrient optimization Plant growth Yield improvement Precision agriculture

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Early Diabetics Prognosis Prediction System Adhering to data Science Utilization Techniques

Early Diabetics Prognosis Prediction System Adhering to Data...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Gopi, S. Naresh, K. Surya Prakash, R. Sakthi Sugan, G. Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Early diabetic prognosis is carried out for the age group between 10 -30 years. To predict the diabetic in early stage we consider the following parameters like Cholesterol, BMI, Blood pressure, Blood glucose levels, Gender, Height, and Weight. On calculating the average values of the parameter, we use random forest algorithm to revert the classified data set with an accurate value for prediction. Since the human body can able to transform the intake of the food in the form of glucose which may affect the diabetic parameter value so the keen observation of the value of the diabetic's parameter must be concentrated. The induction of machine algorithm uses the Uni-variant and Bi-variant variables for the processing of the data. Additionally, support vector classifier (SVC) is used for the unique classification of data set with respect to the diabetic parameters. © 2023 ieee.

关键词： Bi-variant BMI data Science Random Forest SVC Uui-variant

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