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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

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An Effective Implementation and Comparative Analysis of Mobile Robot Path Planning Algorithms

An Effective Implementation and Comparative Analysis of Mobi...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Vigenesh, M. Taneja, Madhur Jayasree, S. Samrat, Bhavuk Padmapriya, G. Kulkarni, Prutha P Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Technology Entc Pune India

ISBN: (纸本)9798350388602

Among the most pressing and intricate challenges in control and computer engineering today is the navigation of autonomous mobile robots. People are now more motivated than ever to use innovative methods, thanks to the development of cutting-edge computational engineering technology. In order to address complicated engineering challenges in the real world, researchers have mostly looked to natural occurrences and their methods for inspiration. In challenging and dense environments, path planning is the best way of defining a safe route in which a mobile robot can navigate to achieve a certain goal from another location. Mobile robots thus require smart and efficient route planning algorithms for proper navigation and self-execution capabilities. In this study, four well-known algorithms for route planning were investigated: Hybrid DA+CS algorithm: The proposed methods DA, CS and PSO using a non-holonomic mobile robot. To test these methods, a test environment has been created with static impediments which will remain constant throughout the experiment. This is because it is where the intended route of the mobile robot is followed and where the availability of the map acquired for path planning algorithms allows assessing and testing them. A comparative study of the following algorithms has been carried out in an effort to understand the trade off between them based on the conventional performance indicators being used. © 2024 IEEE.

关键词： Robot programming

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Secure data communication framework based on fuzzy based secure clustering and hybridized metaheuristic algorithms 4

Secure data communication framework based on fuzzy based sec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vennila, C. Sidhu, Amritpal Preetham, K. Mahajan, Rupali A. Vigenesh, M. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331518981

The Internet of Things (IoT) has made real-time applications more flexible and deployable. IoT has security and energy efficiency issues despite its benefits. This study addresses these issues with a secure data transmission architecture using fuzzy-based secure clustering and hybridised metaheuristic algorithms. The suggested framework contains three phases. The first phase presents an energy-efficient and secure IoT data transfer infrastructure. Secure route selection is done using fuzzy-based secure clustering and the Whale Optimization-based Harris Hawks Optimisation (W-HHO) method. Parallel cypher block chaining and advanced encryption strengthen data security. Secure cluster-based routing is needed for the Internet of Medical Things (IoMT) in the second phase. An adaptive Gaussian mutation-based Sine Cosine Optimisation (SCO) approach clusters nodes, and mutual authentication assures security. Final phase of research delivers optimal and secure fuzzy inference route selection policy. The Levy Flight Distributed optimization-based Henry Gas Solubility Optimisation Algorithm (LFD-HGSO) clusters data, while concurrent encryption and cypher block chaining secure it. Fuzzy inference selects safest paths. Performance tests show that the proposed solutions improve packet delivery rate, throughput, energy efficiency, and network lifetime over conventional methods. These results demonstrate the framework's safe and energy-efficient data transfer for IoT and IoMT applications. Experimental findings show that the suggested strategy outperforms various QoS metrics like packet delivery ratio, throughput, network longevity, and energy usage. Time efficiency is also improved by the PAES-CBC key generation algorithm. © 2024 IEEE.

关键词： Heuristic algorithms

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Signal Quality Prediction for Cognitive Radio Networks using Deep Learning Techniques

Signal Quality Prediction for Cognitive Radio Networks using...

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International Conference on Computing and Networking technology (ICCNT)

作者： Sudhanshu Dev M.P. Santhoshkumar Udita Goyal K N Raja Praveen Kishor R. Pathak G. Ravivarman Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Information Technology Vishwakarma Institute of Technology Pune INDIA Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantastically variable and hard to predict, leading to degraded communication. Currently, deep gaining knowledge of strategies had been proposed as a method to improve the capability of CRNs to correctly expect signal first-rate, allowing multiplied device reliability. On this paper, we gift an assessment of present deep gaining knowledge of tactics to be expecting signal excellent in CRNs, focusing at the structure and application of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and deep reinforcement studying (DRL). The overview additionally examines the overall performance of those processes in terms of accuracy, scalability and generalization to new environments. Subsequently, we provide a survey of capability future guidelines for signal first-rate prediction in CRNs using deep getting to know.

关键词： Deep learning Surveys Performance evaluation Knowledge engineering Accuracy Recurrent neural networks Scalability Cognitive radio Reliability Guidelines

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ajian Liu Zichang Tan Yanyan Liang Jun Wan MAIS Institute of Automation Chinese Academy of Sciences Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets.

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IoT-Based Smart Grids: Technologies, Applications, and Future Perspectives

IoT-Based Smart Grids: Technologies, Applications, and Futur...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Bisen, Archana Al-Attabi, Kassem Esanmurodova, N. Bisht, Deepa Karthikeyan, Kavitha Sharma, Kamal Ushasree, Dupakuntla IES University IES Institute of Technology and Management Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India The Islamic University Najaf Iraq National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Uzbekistan Uttaranchal University Uttaranchal Institute of Management Department of Management Dehradun248007 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India GLA University Department of Mechanical Engineering Mathura India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The union of Internet of Things (IoT) advancements with the space of Shrewd Matrices has catalyzed a change in perspective in the energy scene. This paper thoroughly investigates the synergistic reconciliation of IoT with Shrewd Matrices, clarifying the groundbreaking potential it offers across different aspects of energy the board. Starting with a sharp assessment of the verifiable development and key parts of Shrewd Lattices, the review dives into the urgent job that IoT advancements play in expanding network knowledge, proficiency, and versatility. A basic survey of correspondence conventions, sensor organizations, and information investigation systems lays out the innovative underpinnings empowering consistent data trade inside Brilliant Lattice conditions. Besides, the paper carefully analyzes a range of utilizations, traversing from interest reaction streamlining to powerful dispersion network the executives, and from consistent joining of environmentally friendly power sources to bracing framework security. The work stretches out further to clarify the irreplaceable job of cutting edge metering foundation and ongoing observing frameworks in organizing a responsive and deft lattice biological system. Through a broad overview of true contextual analyses, the paper distils key experiences and best practices from effective IoT executions in Savvy Lattice projects. Expecting the direction of this powerful field, the conversation tests into rising innovations, versatility goals, and the administrative scene, giving an apt outline of future viewpoints. This paper finishes up by highlighting the criticality of continuous exploration attempts in exploring the multi-layered difficulties, especially those relating to security, versatility, and administrative consistence. With an eye toward a maintainable and wise energy future, this thorough assessment of IoT-Based Shrewd Lattices highlights their extraordinary potential as well as calls for coordinated endeavors in pus

关键词： Internet of things

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Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Yelleti, Vivek Vadlamani, Ravi Radha Krishna, P. Centre for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology Castle Hills Road #1 Masab Tank Hyderabad500076 India Department of Computer Science and Engineering National Institute of Technology Warangal506004 India

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution. This unprecedented feature enables them to achieve better diversity and perform global search, effectively yielding a tradeoff between exploration and exploitation. We conducted a comprehensive survey across various publishers and gathered 56 papers. We thoroughly analyzed these publications, focusing on the novelty elements and types of heuristics employed by the extant quantum-inspired evolutionary algorithms (QIEAs) proposed to solve the feature subset selection (FSS) problem. Importantly, we provided a detailed analysis of the different types of objective functions and popular quantum gates, i.e., rotation gates, employed throughout the literature. Additionally, we suggested several open research problems to attract the attention of the *** Codes 68W50, 90C27 © 2024, CC BY-NC-ND.

关键词： Quantum computers

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Listen to the Speaker in Your Gaze

Listen to the Speaker in Your Gaze

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Hongli Yang Xinyi Chen Junjie Li Hao Huang Siqi Cai Haizhou Li Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Technology Xinjiang University Urumqi China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Data Science Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Attending to one’s voice in a cocktail party is notably challenging, particularly for individuals with hearing impairments. This paper proposes a novel eye-controlled target speaker extraction system, which consists of an eye-tracker, face detection model, Active Speaker Detection (ASD), and Target Speaker Extraction (TSE) model. The system employs the eye-tracker to capture real-time video together with the listener’s gaze. This gaze data then allows the face detection model to locate and isolate the target speaker’s face within the video on a frame-by-frame basis. Using the speaker’s face as the reference cue, the system can discern and separate his/her speech from a mixture of multi-talk. The experiments show that the system effectively extracts the target speaker’s speech in complex auditory environments, providing both real-time performance and accuracy. A demonstration of our system is available on our website 1 .

关键词： Mechatronics Random access memory Auditory system Streaming media Real-time systems Data models Face detection

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Advanced functional nanofibers:strategies to improve performance and expand functions

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Frontiers of Optoelectronics 2022年第4期15卷 125-143页

作者： Xinyu Chen Honghao Cao Yue He Qili Zhou Zhangcheng Li Wen Wang Yu He Guangming Tao Chong Hou School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518063China Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridge 02139USA

Nanofbers have a wide range of applications in many felds such as energy generation and storage,environmental sensing and treatment,biomedical and health,thanks to their large specifc surface area,excellent fexibility,and superior mechanical *** the expansion of application felds and the upgrade of application requirements,there is an inevitable trend of improving the performance and functions of *** the past few decades,numerous studies have demonstrated how nanofbers can be adapted to more complex needs through modifcations of their structures,materials,and ***,it is necessary to systematically review the feld of nanofbers in which new ideas and technologies are *** we summarize the recent advanced strategies to improve the performances and expand the functions of *** frst introduce the common methods of preparing nanofbers,then summarize the advances in the feld of nanofbers,especially up-to-date strategies for further enhancing their *** classify these strategies into three categories:design of nanofber structures,tuning of nanofber materials,and improvement of nanofbers ***,the optimization methods,materials,application areas,and fabrication methods are summarized,and existing challenges and future research directions are *** hope this review can provide useful guidance for subsequent related work.

关键词： Functional nanofber Nanofber fabrication Nanofber structure Nanofber materials Nanofber assembly

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A Comprehensive Analysis of Blockchain-Based Cryptographic Framework for Network Security 3

A Comprehensive Analysis of Blockchain-Based Cryptographic F...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Vyshnavi, A. Bharathi, B. Singh, Amanveer Aggarwal, Prateek Yadav, Rakesh Kumar Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Blockchain technology is increasingly developing breakthroughs in secure and decentralized data storage. More recently, it has also garnered interest as a possible solution for network security. In particular, this paper conducts an indepth analysis of a network security cryptographic framework using Blockchain. The framework proposed again adopts the principles of blockchain technology, such as decentralization, immutability and consensus, to build a secure data environment for the network. It uses various cryptographic methods, digital signatures and Encryption and hash functions, to guarantee integrity, confidentiality, and authentication. The framework uses smart contracts to automatically execute security checks that can be enforced with predefined and approved methods. In this analysis, we aim to investigate the proposed scheme's effectiveness, scalability, and practicability in typical network security applications. It will also cover the challenges and future research directions in applying this framework in practical scenarios. This analysis will form the basis for a strong cryptographic system on the Blockchain to provide security reinforcement in networks. © 2025 IEEE.

关键词： Network security

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