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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of Computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Fabrication of PMMA nanocomposite biomaterials reinforced by cellulose nanocrystals extracted from rice husk for dental applications

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Friction 2024年第12期12卷 2808-2825页

作者： Ahmed FOULY Walid M.DAOUSH Hesham I.ELQADY Hany S.ABDO Mechanical Engineering Department College of EngineeringKing Saud UniversityRiyadh 11421Saudi Arabia The King Salman Center for Disability Research Riyadh 11421Saudi Arabia Department of Production Engineering and Mechanical Design Faculty of EngineeringMinia UniversityMinia 61519Egypt Department of Chemistry College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh 11623Saudi Arabia Faculty of Technology and Education Helwan UniversityCairo 11281Egypt Faculty of Engineering Aswan UniversityAswan 81528Egypt Center of Excellence for Research in Engineering Materials(CEREM) King Saud UniversityRiyadh 11421Saudi Arabia Mechanical Design and Materials Department Faculty of Energy EngineeringAswan UniversityAswan 81521Egypt

The primary objective of global studies is to develop the properties and durability of polymers for various *** it comes to dental disability,denture base materials must have sufficient mechanical and tribological performance in order to withstand the forces experienced in the *** work aims to investigate the effects of the addition of low content of cellulose nanocrystals(CNC)on the mechanical and tribological performance of the polymethyl methacrylate(PMMA)*** weight percent of CNC(0,0.2,0.4,0.6,and 0.8 wt%)were added to the PMMA matrix followed by ball milling to evenly distribute the nanoparticles reinforced phase in the matrix *** findings emphasize the significant impact of CNC integration on the performance of PMMA *** increasing the content of the CNC nanoparticles,the mechanical properties of PMMA were *** addition,the tribological outcomes demonstrated a significant reduction in the friction coefficient besides an enhancement in the wear resistance as the weight percentage of nanoparticles *** surface of the worn samples was investigated by utilizing SEM to identify the wear mechanisms corresponding to the different *** addition,a finite elment model(FEM)was developed to ascertain the thickness of the worn layer and the generated stressed on the surfaces of the nanocomposite throughout the friction process.

关键词： polymethyl methacrylate nanocomposite cellulose nanocrystals denture materials rice husk polymethyl methacrylate(PMMA) wear resistance

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Development of a Nail-based Application for the Detection of Disease using Image Processing 2

Development of a Nail-based Application for the Detection of...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gote, Pradnyawant M. Kumar, Praveen Dhale, Trishul Mishra, Gaurav Vedprakash Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of a nail-based detection application employing image processing techniques to detect diseases such as systemic disease, fungal disease, Leukonychia (White spots on nails) and Paronychia (Nail bed infection) through analysis of the patterns of the fingernails and other related symptoms. The developed system integrates several techniques, comprising a customized imaging application, a user-friendly interface, and software for real-time image processing and disease classification. A variety of algorithms proposed for image processing, such as Median filtering, Sobel Mask and HOG features, are designed to extract important features from nail images and then use machine learning models that match these features against a database of established markers of various diseases. Comprehensive testing utilizing an annotated dataset of nail images demonstrated the system's accuracy in classifying certain diseases, further showcasing its potential for possible usage as a proficient tool for early disease diagnosis. Thus, this application is easy to use, user-friendly interface, and provides advancements in diagnosing diseases both in clinical settings and in the comfort of one's home. The incorporation of this technology into routine clinical practice might facilitate the possibility of early diagnosis and early intervention, which could improve patient outcomes and reduce healthcare system burdens. © 2024 IEEE.

关键词： Median filters

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Product platform design for minimum cost in mass customization manufacturing environment 21

Product platform design for minimum cost in mass customizati...

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21st International Conference on Applied Mechanics and Mechanical engineering, AMME 2024

作者： Ismail, T. Saeed, Ahmed Elkharbotly, A. Zamzam, N. Basic and Applied Sciences Department Faculty of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Egypt Design and Production Engineering Department Ain Shams University Egypt

Nowadays manufacturers are facing several challenges to compete in the marketplace. One of the main challenges can be described by the need to offer a range of products and categories in addition to shortening the time during which the product is brought to the customer while maintaining low costs to achieve maximum profitability. Several approaches emerged during the past couple of decades that enabled designers and manufacturers to offer the product variety within a product family on one hand and allowing flexibility and responsiveness at the manufacturer's side on the other hand. This study addressed the problem of product platform design with the objective of minimizing the manufacturing costs incurred in the platform manufacturing phase and the mass customization phase. A genetic algorithm model that minimizes the total manufacturing cost is developed where optimum product platform design is determined. The developed model is used to study the effect of demand proportion of different product variants within a product family on the platform design and the total manufacturing cost. © Published under licence by IOP Publishing Ltd.

关键词： Smart manufacturing

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Comparative Study of Mechanical Properties for Steel S50C by Induction Heating Application 11th

Comparative Study of Mechanical Properties for Steel S50C by...

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11th International Conference on Creative technology, ICCT 2023

作者： Klaywanna, Kanda Kumkoon, Piyapong Chakkuchan, Panithan Thandngarn, Charn Department of Furniture Technology and Design Faculty of Science and Technology Rajamangala University of Technology Krungthep Bangkok Thailand Department of High Precision Manufacturing Engineering Faculty of Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Aircraft Maintenance Engineering Faculty of Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Industrial and Mechanical Engineering Faculty of Engineering Rajamangala University of Technology Krungthep Bangkok Thailand

ISBN: (纸本)9783031591631

In this paper, a study of the properties of S50C steel used for mold structure is presented, and plastic injection molds are prepared to be hardened by giving it a low surface hardness. There is a risk of warping and cracking when heat treatment causes the surface to be hardened for milling, turning, and hardening processes. By receiving heat from two sources: the heat from the heater and induction heating. When heating from a heater that can reach a temperature of 600–800 ℃ with induction heating, using an inverter circuit to adjust the power and control the temperature for heat, studying the internal structure of steel when heated, and hardening test results of the S50C steel structure by using a laser confocal microscope to see the comparison results of unheated steel, heated steel with a heater, and steel that has been heated by induction heating. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Induction heating

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Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances 1

Navigating the Nexus: Exploring the Fusion of AI and Nanotec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Izanker, Sakshi V. Dhole, Aman Kumar, Praveen Faculty of Engineering and Technology Department of Computer and Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration. © 2023 IEEE.

关键词： Medical applications

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