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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Manish Rai Hemlata Parmar Ansh Jain KuljeetSingh Shekhawat Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This review paper explores the transformative role of financial technology (fintech) in the financial services industry, encompassing banking, financial services, and market intermediaries like stock exchanges, brokers, and research houses. Fintech, closely intertwined with data science, has redefined traditional processes and reshaped the financial landscape through innovative models. The paper delves into how machine learning, artificial intelligence, and predictive analytics have improved risk assessment, decision-making, and market resilience within the banking sector. By integrating advanced data science techniques, financial institutions have enhanced their ability to predict risks and mitigate market fluctuations, leading to greater overall stability. From algorithmic trading to sentiment analysis, data-driven approaches have introduced a new era of transparency, efficiency, and accessibility in financial markets. The paper also highlights pivotal innovations in fintech, tracing its evolution and impact on stock market dynamics. Ultimately, this review emphasizes how fintech has driven continuous change in the financial services industry, challenging conventional paradigms and fostering a dynamic, data-driven environment for investment and financial decision-making.

关键词： Technological innovation Automation Reviews Heuristic algorithms Decision making Data science Stability analysis Risk management Stock markets Resilience

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Machine Learning-based Framework for Malware Detection in Critical Infrastructures for Smart Cities

Machine Learning-based Framework for Malware Detection in Cr...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Bhatt, Aditya Dasadiya, Sujal Gohil, Aditya Gupta, Rajesh Kumar Jadav, Nilesh Tanwar, Sudeep Garg, Deepak Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India SR University School of Computer Science and AI Telangana Warangal India

ISBN: (纸本)9798350361537

Technology is an inseparable part of today's world and is extensively used in every conceivable domain to provide a wide range of applications. Technology has many critical infrastructures, which serve the function of delivering crucial services essential for the smooth functioning of smart cities. These infrastructures produce a large amount of data which is extremely sensitive and very potentially catastrophic if it falls into the wrong hands. Various techniques and concepts have been introduced and developed to deal with the detection of malicious data. But with the passing time, malware threats grow in both frequency and complexity, machine learning and artificial intelligence techniques can be combined to enhance malware detection. Many Machine Learning algorithms exist which may be confusing for users. Different algorithms vary in their results, accuracy, and prediction capabilities depending on the data they are trained and tested on. For data produced by critical infrastructures, the prediction must be as accurate as possible. Encouraged by the aforementioned facts, we propose an approach aiming to contrast and compare different machine learning algorithms for malware detection on portable executable files dataset. Different performance metrics are compared for all the considered algorithms, based on which, the best algorithm for critical infrastructure malware detection can be determined. © 2024 IEEE.

关键词： Adversarial machine learning

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An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model 4

An Innovative Method for Brain Stroke Prediction based on Pa...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kavitha, K. Devi, Vanjangi Jyotsna Alam, Mohammad Shabbir Sindhu, V. Gandhewar, Nisarg Kalra, Gourav Gmr Institute of Technology Department of CSE-AI&ML Andhra Pradesh Razam India Aditya Institute of Technology and Management Department of Information Technology Andhra Pradesh Tekkali India College of Engineering and Computer Science Jazan University Department of Computer Science Jizan Saudi Arabia Christ University Department of Computer Science Karnataka Bangalore India Shri Ramdeobaba College of Engineering and Management Department of Aiml Nagpur India Department of Mechanical Engineering Mullana Ambala India

ISBN: (纸本)9798331540364

Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. © 2024 IEEE.

关键词： Contrastive Learning

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A CNN based deep learning model for detecting P2P Botnets using flow features 2

A CNN based deep learning model for detecting P2P Botnets us...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Mohan, H.G. Kumar, Jalesh Rajesh, I.S. Geethanjali, K Sai Krishnamurthy, Manjunath Sargur Jagannathan, K. Jnn College of Engineering Department of Computer Science and Engineering Karnataka Shivamogga India Bms Institute of Technology & Management Department of Ai&ml Karnataka Bangalore India Jp Morgan & Chase Co HoustonTX United States Cognizant Karnataka Bangalore India

ISBN: (纸本)9798350372892

A botnet is a network of computers managed by a botmaster or a command and control (C&C) server. Botnets are a big concern on the cyber security. The Peer-to-peer (P2P) applications have made considerable advances in these days. The P2P networks are is widely used for file sharing and multimedia applications, because to its high transmission rate and robustness. The P2P botnets are typical examples of harmful malwares directed to execute different harmful operations. In this work, a novel Convolutional Neural Networks (CNNs) model is used to identify the P2P botnets using traffic flow features. Unlike traditional botnet detection methods, the CNN model incorporates the spatial feature representations, providing a comprehensive analysis of network behavior. The model is validated on BoT-IoT dataset to detect botnets along with its categories and subcategories of attacks. The model achieved 99% of accuracy and an AUC of 0.94. Through ROC plots it is evident that the model is robust in identifying the category and subcategory of attacks. The experimental results demonstrate that our CNN model significantly outperforms existing state-of-the-art methods in terms of detection accuracy and false positive rate. © 2024 IEEE.

关键词： Convolutional neural networks

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Motion planning

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Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data 41

Consistent Diffusion Meets Tweedie: Training Exact Ambient D...

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41st International Conference on Machine Learning, ICml 2024

作者： Daras, Giannis Dimakis, Alexandros G. Daskalakis, Constantinos Department of Computer Science University of Texas Austin United States Archimedes AI United States Department of Electrical and Computer Engineering University of Texas Austin United States Department of Electrical Engineering and Computer Science MIT United States

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance. Copyright 2024 by the author(s)

关键词： Problem solving

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A Neural Computing-Based Access Control Protocol for ai-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (ai) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of ai-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for ai-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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An Enhancemnt of Low-Light Images Using Hierarchical Pyramid Attentive Network and Hierarchical Feature Fusion 2

An Enhancemnt of Low-Light Images Using Hierarchical Pyramid...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Suma, K.G. Nagendar, Y. Habelalmateen, Mohammed I. Revanasiddappa Halvi, Srinivas Vit University School Of Computer Science And Engineering Dept. Of Ai & Ml Andhra Pradesh India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Dayananda Sagar College Of Engineering Department Of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798350381665

The development of the low-light images assists efficiently in high visioned tasks which often fails in the poor illumination conditions. Most of the old methods, applied has enhanced the low-lighted degrade images which resulted in the undesirable enhancement including the intensive noise, blurred details and slanted color. This paper is used for finding alignment to an end-to-end image along with enhancement of the perceptual information by pixel wise. To reduce the differences in style, Hierarchical Pyramid Attentive Network (HPANet) was used, which results in the betterment of the illuminations and the reveal exposed information's. After aligned images, a hierarchy pyramid enhancements subnetwork is used for optimizing the quality of the image, which helps for removing the noises, results in enhancement low light images. A Hierarchical Feature Fusion (HFF) module is proposed which results in the high resolution of the images in perceptual qualities. Extensive experiments have also performed that effectiveness of HPANet-HFF methods on various datasets have outdone the existing methods in reproduction of the color. According to the results, the proposed HPANet-HFF obtained MAE and RMSE of 106.5 and 181.5 which is superior over other existing architectures. © 2024 IEEE.

关键词： Cell proliferation

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Decision trees

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