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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek G D Nithyashree S A Rajashekhar Pankaj Zanke K S Navyashree Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm’s iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm’s exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms Scalability Whales Whale optimization algorithms Scheduling

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Context Gating in Spiking Neural Networks: Achieving Lifelong learning through Integration of Local and Global Plasticity

arXiv

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arXiv 2024年

作者： Shen, Jiangrong Ni, Wenyao Xu, Qi Pan, Gang Tang, Huajin The State Key Lab of Brain Machine Intelligence College of Computer Science and Technology Zhejiang University Zhejiang310027 China The Faculty of Electronic Information and Electrical Engineering School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for their energy efficiency and biological plausibility. To overcome catastrophic forgetting when learning multiple tasks in sequence, current SNN models for lifelong learning focus on memory reserving or regularization-based modification, while lacking SNN to replicate human experimental behavior. Inspired by biological context-dependent gating mechanisms found in PFC, we propose SNN with context gating trained by the local plasticity rule (CG-SNN) for lifelong learning. The iterative training between global and local plasticity for task units is designed to strengthen the connections between task neurons and hidden neurons and preserve the multi-task relevant information. The experiments show that the proposed model is effective in maintaining the past learning experience and has better task-selectivity than other methods during lifelong learning. Our results provide new insights that the CG-SNN model can extend context gating with good scalability on different SNN architectures with different spike-firing mechanisms. Thus, our models have good potential for parallel implementation on neuromorphic hardware and model human's behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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machine learning in Healthcare: A Review of Current Applications and Future Trends

Machine Learning in Healthcare: A Review of Current Applicat...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Prajyot Yesankar Chetan Puri Aditya Barahate Pradanywant M. Gote Janhavi Hajbe Adesh Pawar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

machine learning (ML) has been a main force behind important breakthroughs in patient tracking, personalized medicine, medical tests, and operating efficiency in the healthcare business in recent years. machine learning algorithms provide unique insights into early disease identification, picture analysis, and prediction analytics, improving diagnosis accuracy and treatment results. These programs are able to examine big and complex information. By predicting treatment effectiveness and genetic risks, machine learning (ML) allows personalized medicine by allowing customized care through tailored therapy approaches. Furthermore, preventative health management is backed by ML-driven prediction analytics and online patient tracking, especially for chronic illnesses. Operationally, machine learning increases process efficiency and cuts costs, allowing for data- driven decision-making and individualized patient care. However, there are important ethics issues with machine learning acceptance in the healthcare business as well, such as data protection, computer bias, and legal problems. This study offers a full analysis of the state-of-the-art machine learning applications in healthcare, stressing their revolutionary potential while discussing the ethics issues and hurdles that must be addressed to ensure execution that is both responsible and fair.

关键词： Ethics machine learning algorithms Law Hospitals Precision medicine Data protection machine learning Medical services Predictive analytics Computer security

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A Comprehensive Evaluation of Fake Face Recognition Scheme using artificial intelligence Oriented learning Scheme 1

A Comprehensive Evaluation of Fake Face Recognition Scheme u...

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1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Subashree, S. Rose S, Remya Valarmathy, A.S. Joseph, Princy Dennis, P. Jasmin Ravi, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India R.M.D. Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India CMR College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Vi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Saveetha University Department of VLSI Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350365016

Fake face recognition has emerged as a critical area of research due to the proliferation of synthetic media and its potential misuse in various domains. This study presents a thorough evaluation of a novel Fake Face Recognition Scheme utilizing artificial intelligence Oriented learning Scheme (AIOLS) coupled with Generative Adversarial Network (GAN)-GoogleNet integration. The aim is to discern authentic facial images from synthetic or tampered ones with high accuracy. In this research, we employed a dataset comprising a diverse range of authentic and synthetic facial images. The proposed scheme incorporates the power of GANs for generating synthetic facial images and leverages GoogleNet, a state-of-the-art deep convolutional neural network, for discriminative feature extraction. The integration within the AIOLS framework facilitates efficient learning and adaptation to complex patterns present in fake facial images. The evaluation of our scheme was conducted through rigorous experimentation on the dataset, employing various performance metrics. Notably, the accuracy obtained was measured at an impressive 96.7%, signifying the effectiveness of our approach in distinguishing between real and fake facial images. Additionally, other metrics such as precision, recall, and F1-score were also computed to provide a comprehensive assessment of the scheme's performance. © 2024 IEEE.

关键词： Generative adversarial networks

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A Novel Approach for Medical Image Encryption Using Multiple Chaotic Maps and DNA Diffusion Operations

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Pattern Recognition and Image Analysis 2025年第2期35卷 94-105页

作者： Biradar, Sumangala Akkasaligar, Prema T. Biradar, Sunanda Department of CSE(Artificial Intelligence and Machine Learning) BLDEA’s V.P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapur India Department of Computer Science and Engineering KLE Technological University’s Dr. M.S. Sheshgiri College of Engineering and Technology Belagavi Campus Belgaum India

Due to advancement in electronic interaction, the broadcast of a medical image is very common. The electronic interaction is open to very one and hence, it is risk for medical image communication. The communication of medical image through opensource network adds uncertain risk of undesirable effect and causes important disease related facts in the image being lost or corrupted. For medical practitioner, diagnosing the correct disease from the lost or corrupted medical image. Hence high-level security and verifying integrity is very essential. A new cryptosystem based on DNA cryptography and multiple high dimensional chaotic maps and SHA-256 is proposed to deliver high-level security and verify integrity. The medical image is divided into two subimages. The subimages are converted into DNA synthesis using dynamic DNA coding rules. The hash keys are generated for both DNA synthesis using SHA-256. The Chen’s chaotic sequences are utilized to permute the pixels of DNA synthesis of subimage one. Another DNA synthesis of subimage two are permuted using Lorenz chaotic sequences. The hash keys are XORed with chaotic sequences. The DNA diffusion operation diffuses the pixels of the DNA synthesis. The dynamic DNA decoding rules are used to get cipher image. The simulation outcomes prove that the proposed cryptosystem is suitable to provide high-level security and integrity. The cryptanalysis confirms that proposed system is invulnerable to various types of attacks.

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Active learning of Robot Vision Using Adaptive Path Planning

arXiv

引用

arXiv 2024年

作者： Rückin, Julius Magistri, Federico Stachniss, Cyrill Popović, Marija Institute of Geodesy and Geoinformation Cluster of Excellence PhenoRob University of Bonn Germany MAVLab Faculty of Aerospace Engineering TU Delft Netherlands University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of domains and limits the robot’s vision performance during missions. Recently, self-supervised as well as fully supervised active learning methods emerged to improve robotic vision. These approaches rely on large in-domain pre-training datasets or require substantial human labelling effort. To address these issues, we present a recent adaptive planning framework for efficient training data collection to substantially reduce human labelling requirements in semantic terrain monitoring missions. To this end, we combine high-quality human labels with automatically generated pseudo labels. Experimental results show that the framework reaches segmentation performance close to fully supervised approaches with drastically reduced human labelling effort while outperforming purely self-supervised approaches. We discuss the advantages and limitations of current methods and outline valuable future research avenues towards more robust and flexible robotic vision systems in unknown environments. © 2024, CC BY.

关键词： machine vision

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Algorithmic Crypto Trading using EMA Strategy

Algorithmic Crypto Trading using EMA Strategy

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Revathi S Shobha Meesala Sudha V Priyanka R Manikandakumar M Vigneshwaran T Computer Science & Engineering (AIML) CMR Institute of Technology Bengaluru India Computer Science & Engineering REVA University Bangalore India Artificial Intelligence & Machine Learning Cambridge Institute of Technology Bangalore India AI ML and Data Science Christ University Bangalore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework.

关键词： Deep learning Pervasive computing Social networking (online) Profitability Navigation Data visualization Manuals Cryptocurrency Risk management Portfolios

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machine learning Induced Selective Identification of Dopamine Using Doped Carbon Quantum Dots

Machine Learning Induced Selective Identification of Dopamin...

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International Conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Ruma Das Abhirup Paria P. K. Giri Department of Applied Science (Physics) Haldia Institute of Technology Haldia India Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Haldia Institute of Technology Haldia India Department of Physics & Centre for Nanotechnology Indian Institute of Technology Guwahati Guwahati India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The present work highlights the selective identification of dopamine (DA) with machine learning (ML) classification. Dopamine is an essential organic neurotransmitter that acts a dynamic role. With the inhouse experimental da-ta, the ML models are trained with more than thirty classification algorithms for the efficient selection of DA among various metal ions and biomolecules that exist in human body fluids. The experimental data was collected by measuring the turn-off photoluminescence intensity of nitrogen-doped car-bon quantum dots in the presence of various metal ions and biomolecules. A promising agreement between predicted result and experimental consequences endorses the effectiveness of the proposed ML prediction. Additionally, confusion matrices and receiver operating characteristic (ROC) are introduced to validate the outcomes by visual analysis.

关键词： Visualization Biological system modeling Quantum dots Metals Molecular biophysics machine learning Photoluminescence Ions Classification algorithms Sensors

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Code Smell Detection Using Whale Optimization Algorithm

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Computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： Software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Human Factors engineering Simulated Analysis in Administrative, Operational and Maintenance Loops of Nuclear Reactor Control Unit Using artificial intelligence and machine learning Techniques

SSRN

引用

SSRN 2024年

作者： Khamaj, Abdulrahman Ali, Abdulelah M. Saminathan, Rajasekaran Shanmugasundaram, M. Industrial Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore India

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective neural network models for machine learning program are suggested for administrative, operational and maintenance loops of nuclear reactor control unit. © 2024, The Authors. All rights reserved.

关键词： machine learning

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