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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Menaka, S. Harshika, Jonnalagadda Philip, Sarah John, Rashi Bharathiraja, N. Murugesan, S. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Tagore Engineering College Department of Information Technology Rathinamangalam Chennai India

ISBN: (纸本)9781665462167

Phishing attacks are now one of the prevalent dangers that firms, service providers and internet users must deal with. Rather than targeting software vulnerabilities, it targets human vulnerabilities. It is the act of enticing users to attain their personal data using fake emails and websites. Like how e-commerce sectors are growing, phishing attacks are also developing. Preventing phishing attempts is a critical aspect of protecting online transactions. Since hacktivists, spy agencies and cybercriminals now have a rich field in which they can operate sophisticated phishing attacks, prompt detection of phishing attempts is more critical than ever. To properly respond to various phishing attacks, it is required to gain a thorough understanding of these attacks, and suitable response techniques must be used. The challenges faced in this research is finding the appropriate datasets and Feature extraction prompted the study of several modules, in addition to understanding every module and attaining the desired outcome from it. Machine learning techniques are used to accurately identify phishing attacks before cause harm to a user. Being able to handle the changing nature of phishing attempts and offering an accurate method of classification, it is one of the most practical ways to approach the situation. © 2023 IEEE.

关键词： Websites

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DNNT (Deep Neural Network for Telugu): a framework for speech recognition of Telugu language with parallel computing approach

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International Journal of Speech Technology 2025年 1-9页

作者： Reddy, S. Satheeswara Mnoj, Sheik Khadar Ahmad Rao, Karu Prasada Department of MCA Dr. Lankapalli Bullayya PG College Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering GST GITAM Deemed to be University Andhra Pradesh Visakhapatnam India

The revolutionary growth in smart devices and increase in social media led to growth of online voice-based applications and online speech communication. The rapid advancement of artificial intelligence and machine learning has significantly impacted speech processing applications, enabling seamless human-computer interaction. This paper presents a Telugu Speech Recognition and Synthesis System employing a hybrid approach integrating Hidden Markov Models (HMM) and Deep Neural Networks (DNN). The system performs Speech-to-Text-to-Speech (STTS) conversion, ensuring accurate speech recognition and synthesis. The proposed model preprocesses Telugu speech data through noise reduction, silence removal, and resampling techniques. Mel-Frequency Cepstral Coefficients (MFCCs) are extracted as feature representations, followed by HMM-based phoneme modelling and DNN-based classification for robust recognition. Experimental results have shown performance in accuracy, computational efficiency, and naturalness of generated speech. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech synthesis

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Heart Arrhythmia Classification Using Machine Learning Algorithms 2

Heart Arrhythmia Classification Using Machine Learning Algor...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Hiremani, Vani Devadas, Raghavendra M. Hemashree, L. Sridevi, S. Pasha, Afroz Pune India School of Engineering Presidency University Computer Science Department Karnataka Bangalore India Dayanand Sagar college of Engineering Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350344882

The normal heart rate in humans is between 60 and 100 beats per minute. Any variation in this range is referred to as a heart arrhythmia in medicine. This research aims to categorize arrhythmia into 16 distinct categories using SVM, KNN, and Nave Bayes like machine learning techniques. The feature-rich UCI Arrhythmia dataset is considered, which has been reduced using the Boruta feature selection technique. The consideration of only relevant features has boosted an accuracy of 9.9% in the SVM model, 3.3% in the KNN model, and 24.2% in the Nave Bayes model. Multiclass classification is performed here while taking all of the original characteristics into account from the dataset and omitting non prominent information. The performance of three algorithms is compared using accuracy and kappa ratings. After performing feature selection, the results demonstrate comparative performance between SVM (accuracy 71.3%) and Naive Bayes (accuracy 70.4%), with KNN method having the lowest accuracy of 62.6%. © 2023 IEEE.

关键词： Classification (of information)

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Comparison of Tribological Behavior of Hard Faced Fe-Based Metallic Composite Coatings Sprayed Using HVOF

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-18页

作者： Soni, Piyush Kumar Prasad, C. Durga Manjunath, H.R. Tiwari, Amit Raju, Vadali Pitchi Athithan, Senthil Deshmukh, Khemraj Kumar, Anjani Department of Mechanical Engineering New Horizon College of Engineering Karnataka Bengaluru India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka Bengaluru India Department of Mechanical Engineering Ramaiah Institute of Technology Karnataka Bengaluru560054 India Department of Mechanical Engineering Suresh Gyan Vihar University Jaipur India Mechanical Engineering Department & amp Principal Indur Institute of Engineering and Technology Siddipet India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Parul University Gujarat Vadodara India Mechanical Engineering Department Government Engineering College Arwal India

Fe-based alloys have recently become well-known for their ability to resist wear and corrosion. Iron, the primary component of alloys, is a cheaper option than alloys made from Co and Ni. There is a need for affordable and easily obtainable coatings based on iron. Coatings in the Fe–Ni–Cr system have demonstrated superior wear resistance compared to other commonly used Fe-based metallic coatings. The Fe-12Ni17Cr2.5Mo2.3Si0.03C based (Metco 41C) metallic coatings were applied onto boiler steel substrates using the High Velocity Oxy-Fuel (HVOF) spraying method. The coatings were examined using X-ray diffraction (XRD), a scanning electron microscope (SEM), and a microhardness tester to determine their density, porosity, microstructure, hardness, and surface roughness. The sliding wear behavior of the substrate and coatings was studied using the pin-on-disk tribometer. Figuring out the wear loss and coefficient of friction. Comparison was made of wear results between the substrate, coatings, and composite coatings. By combining these carbide powders with 30% SiC and WC-12Co particles, the coating’s wear resistance is enhanced in comparison to the Metco 41C coating. In comparison to boiler steel, the Metco 41C + SiC and Metco 41C + WC–Co coatings exhibited a roughly 70% and 40% reduction in volume loss compared with boiler steel respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： HVOF thermal spraying

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Optimizing Different Methods of Fabricating 3D Printed UV Blocking Sheets for Solar Cells 1

Optimizing Different Methods of Fabricating 3D Printed UV Bl...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： Alhindi, Amjad Fawzi Elatab, Nazek Baawad, Lujain Khalid Bahabry, Rabab R. Allam, Fahad Laboratory Electrical and Computer Engineering Computer Electrical Mathematical Science and Engineering Thuwal Saudi Arabia College of Science Department of Physical Sciences Jeddah21589 Saudi Arabia Materials Science and Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9798331519445

This study investigates the fabrication of innovative UV-blocking sheets that effectively transmit visible light while simultaneously obstructing harmful ultraviolet (UV) radiation, utilizing Cerium Oxide (CeO2) and Zinc Oxide (ZnO) nanoparticles as UV-blocking agents. Three distinct fabrication methods were explored: The Powder Method, where nanoparticles were incorporated directly into polydimethylsiloxane (PDMS);the Ball milling Mixture method, which involved mixing nanoparticles through ball milling;and the Solvent-Based Mixture method, where nanoparticles were dissolved, mixed, and dried;before incorporation into PDMS. Samples were produced using a 3D printing technique, which is Direct Ink Writing (DIW) and characterized for their optical properties via UV-Vis spectroscopy. The results indicated CeO2 demonstrated superior UV-blocking capabilities (a maximum blockage of ~99%) compared to ZnO, while maintaining high transmittance for visible light (a maximum of ~90%). Notably, the solvent-based method exhibited better results than the ball milling method;however, the powder method had the best results. Therefore, this investigation offers a promising solution for solar panel protection that aligns with Saudi Arabia's Vision 2030 energy objectives. The relentless exposure of devices, particularly solar cells, to harmful UV radiation poses a formidable challenge and leads to annual degradation rates of between −0.8% and −4.9%, besides other factors. These findings underscore the critical importance of developing effective UV-blocking solutions. ©2024 IEEE.

关键词： Polydimethylsiloxane

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Contrastive Loss Function Siamese Network for Change Detection on Remote Sensing Images 2

Contrastive Loss Function Siamese Network for Change Detecti...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Alkhayyat, Ahmad Shreyas, A.V. Raviprakash, M.L. Jayachandran, T. Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Nandha Engineering College Department of Ece Erode India

ISBN: (纸本)9798350373110

To observe the changes in the earth surface is increasing due to development of navigation software applications and also mainly prediction purposes for agriculture purpose and environmental hazards. To overcome the problem one of the most crucial technological choices for effectively identifying changes in observations in remote sensing photos is Change Detection (CD). Applications for CD are numerous and include mapping disasters, environmental monitoring, urban planning and land use study. On the other hand, the CD applications face significant hurdles due to the often-occurring class imbalance issue. In order to tackle this problem, a new type of Contrastive loss function Siamese network (CLFS-Net) that integrates the links between scenes and foregrounds to yield global-aware features for effective change detection. The CLFS-Net improve symbiotic connection learning between scene and foreground while simultaneously learning contextual relationships. The proposed CLFS-Network gained in terms of accuracy as 90%, precision as 95% and recall as 91% and f1 score as 95% respectively. © 2024 IEEE.

关键词： Environmental monitoring

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Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

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IEEE Journal of Biomedical and Health Informatics 2024年第7期28卷 4084-4093页

作者： Dorjsembe, Zolnamar Pao, Hsing-Kuo Odonchimed, Sodtavilan Xiao, Furen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan University of Tokyo Faculty of Engineering Tokyo113-8654 Japan College of Medicine Institute of Medical Device and Imaging National Taiwan University Taipei100 Taiwan

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. © 2013 IEEE.

关键词： Semantics

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Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 313-336页

作者： Demir, Gülay Chatterjee, Prasenjit Kadry, Seifedine Abdelhadi, Abdelhakim Pamučar, Dragan Vocational School of Health Services Sivas Cumhuriyet University Sivas Turkey Department of Applied Data Science Noroff University College Norway Department of Mechanical Engineering MCKV Institute of Engineering West Bengal Howrah711204 India Engineering Management Department College of Engineering Prince Sultan University Saudi Arabia Széchenyi István University Győr Hungary

This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Expert systems

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