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Advancement in technology (ICONAT), International Conference for

作者： S. ramya M. radhika Subramani roychoudri S. Senthil G. Arunachalam M. Muthulekshmi Department of Cyber Security Paavai Engineering College Namakkal Tamil Nadu India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Usha Rama College of Engineering and Technology Vijayawada Andhra Pradesh India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

An innovative architecture that combines Long Short-Term Memory (LSTM) networks with Internet of Things (IoT) technology is presented in this paper to assist dementia patients. It solves Human-robot Interaction (HrI) difficulties in dementia care. It also ensures security and responsiveness in HrI to establish a secure and trustworthy environment for dementia patients. LSTM networks help the robot to recognize and to adapt based on the dementia patients’ behaviors. Through real-time data gathering and exchange, IoT devices offer seamless and context-aware interactions. The system protects user privacy and sensitive data with strong encryption and authentication processes. The framework uses adaptive learning methods to improve the robot’s comprehension of the user’s preferences and needs, offering a personalized and responsive assistance system. Simulations and real-world situations show that the proposed technology provides safe, responsive, and personalized human-robot interactions for dementia care. By presenting a potential solution to dementia care difficulties it advances assistive technology and improves quality of life for dementia patients.

关键词： Training Adaptation models Human-robot interaction robot sensing systems real-time systems Internet of Things security Long short term memory dementia resilience

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deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M. Balasubramani B. J. Job Karuna Sagar K. C. rajheshwari E. Saranya B. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MrI. More specifically, U-Net tiles are pre-trained using a big Brain COLOr atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited Brain COLOr atlas. We also elaborate on his method for processing Mr images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull resonance Telecommunication computing

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detection of Brain Haemorrhage Using Convolutional Neural Network

Detection of Brain Haemorrhage Using Convolutional Neural Ne...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Mahesh Prasanna K Gadidala Venkatesh Kandle Navaneetha N. Naveena F. Sheeja Mary Balambigai Subramanian Department of CSE Vivekananda College of Engineering and Technology Puttur DK Karnataka India Department of CSE (Cyber Security) CVR College of Engineering Hyderabad Telangana India Department of CSE (AI&ML) CVR College of Engineering Hyderabad Telangana India Department of CSE Kongu Engineering College Erode TN India Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai TN India Department of ECE Karpagam College of Engineering Coimbatore TN India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

recently, deep learning calculations have been used for picture identification and detection, with excellent results in the pharmaceutical industry, such as clinical image research and analysis. For patients with brain haemorrhages, prompt diagnosis of the drain type and application of the appropriate therapy is essential to enhancing their chances of long-term survival. It has been shown that machine learning models are incredibly useful for assisting doctors in the management of intracranial haemorrhages. We evaluate a few 2-dimensional (2d) convolutional neural organisations (CNNs) in this research. In order to identify cerebral haemorrhage in computed tomography (CT) images, this research will use convolutional neural networks (CNN) and deep learning computations.

关键词： Training deep learning Accuracy Computed tomography Computational modeling Medical treatment Predictive models Brain modeling Convolutional neural networks Pharmaceutical industry

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A robust design of Cooperative Model to Enhance Energy Efficiency over Heterogeneous 5G Wireless Sensor Network Paradigm

A Robust Design of Cooperative Model to Enhance Energy Effic...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： C. Anitha G. M. Karthik M. Uma Maheswari L. Vinoth Kumar G. Sasikala Jayanthi S Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Multiple interconnecting Wireless Sensor Networks (WSN) built on the same region have grown increasingly prevalent in recent years due to the widespread diffusion of 5G Wireless Sensor Networks (WSNs). Cooperative packet forwarding is anticipated to prolong their lifespan in this scenario. While some studies have looked at 5G network models that work together, the majority of these studies ignore the fact that WSNs vary greatly in terms of factors like battery life, starting time of operation, number of nodes, locations of nodes, energy consumption, packet size and/or data transmission timing, and so on. Optimistic lifelong gains through teamwork could not hold water in a diverse setting. Improve Energy Efficient Communication Paradigm (EEECP) is a fair cooperative routing approach that we offer in this study for 5G wireless sensor networks that are diverse and overlapping. The efficiency of this suggested approach will be evaluated by cross-validating it with the usual Cooperative data Communication (CdC) model of communication. In order to keep the overall energy usage down, it implements a cooperative forwarding system and an energy-pool. The energy pool mediates equitable collaboration. Last but not least, the suggested strategy performs exceptionally well according to the simulation findings.

关键词： Wireless sensor networks Energy consumption Technological innovation 5G mobile communication Array signal processing data models Energy efficiency

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Advancing security: Exploring AI-driven data Encryption Solutions for Wireless Sensor Networks

Advancing Security: Exploring AI-driven Data Encryption Solu...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： L Arulmurugan Sangeeta Thakur r. dayana S Thenappan Banavath Nagesh r. Kalaivani Sri Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Electronics and Communication Engineering Garhwa Polytechnic Hasker Jharkhand India Department of Computer Science and Engineering Jeppiaar Institute of Technology Sriperumbudur Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Cyber Security Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana India Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The research thoroughly examines encryption techniques in Wireless Sensor Networks (WSNs), emphasizing machine learning-based, conventional, and cutting-edge AI-driven approaches. Important conclusions are drawn from a comparative analysis of speed, overhead, and energy use. The machine learning approach shows significant flexibility and increased security, whereas the conventional approaches show subtle trade-offs between energy use and network latency. This study presents a revolutionary AI-driven encryption architecture and reveals improved performance measures, such as reduced overhead, maximum energy efficiency, and faster speed. The outcomes demonstrate how AI can completely transform WSN security by providing a flexible and reliable solution. Graphics improves the findings’ comprehensibility and offers a more sophisticated view of performance differences. This study establishes a standard for current practices and opens the door for further developments in WSN security through AI integration.

关键词： Graphics Wireless communication Wireless sensor networks data security Machine learning Transforms Energy efficiency

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Swift Heavy Ion Irradiation of Polyaniline-Graphene Nanocomposite Films: Structural and Optical Properties

SSRN

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SSrN 2023年

作者： Chilukusha, daniel Chilufya Mboukam, J.J. Maphiri, Vusani M. Manyala, Ncholu Msimanga, Mandla PV NanoComposites R&D Platform Department of Physics Tshwane University of Technology P Bag X680 Pretoria0001 South Africa Department of Physics Mulungushi University Kabwe10101 Zambia NRF iThemba LABS TAMS Private Bag 11 JohannesburgWITS 2050 South Africa Department of Physics University of Pretoria Pretoria0002 South Africa

In this study, we investigate the use of swift heavy ions to modify the structural and optical properties of PANI-graphene nanocomposite (PANI-G NC) films. PANI-G NC films were synthesized on Indium Tin Oxide (ITO)-coated glass by electrochemical polymerization of aniline in the presence of CVd-grown graphene. The films were then irradiated with 36 MeV Cu8+ ions at fluences between 5.4 × 1012 and 6.4 × 1013 ions/cm2. SEM results show that irradiation up to 1.6 × 1013 ions/cm2 leads to the formation of a porous interconnected network, with graphene nanoparticles adhering to the PANI matrix. Beyond this fluence, a dense and compact granular structure appears. raman spectroscopy reveals enhanced raman scattering of the polaronic lattice and the introduction of defects in graphene. At fluences exceeding 2.1 × 1013 ions/cm2, the obtained spectra reveal a distinctive broad envelope centered at the d and G positions of graphene, characteristic of nanocrystalline graphite. X-ray diffraction shows an increase in both crystallinity and crystallite size of the PANI-G NC film with increasing fluence, followed by a decrease beyond a critical ion fluence. Optical modifications were characterized by a red shift in the UV-Vis spectrum with increased fluence. The optical band gap shows a decreasing trend with ion dose, attributed to the formation of carbonaceous clusters along the latent tracks of energetic ions. In short, this study demonstrates that swift heavy ion irradiation could be an effective tool for enhancing the structural and optical properties of PANI-G NC films and tailoring them for specific applications. © 2023, The Authors. All rights reserved.

关键词： Graphene

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Enhanced security Protocol for VLSI Systems: Modified AES Algorithm for robust data Transmission

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jinsha Lawrence dinesh Kumar Budagam P. Mukilan Kavitha Veerappan E. Chandrasekhar K. Barathi Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore India Department of MS in Software Engineering Security Architect at VISA California United States Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology (A) Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance.

关键词： Protocols Power demand System performance Very large scale integration Throughput Hardware Encryption security data communication Standards

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Secure Audio Steganography with reversible data Hiding and Encryption

Secure Audio Steganography with Reversible Data Hiding and E...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： P. S. Arthy K. Sanjai Kumar S. Vinson Joshua G. Sahaana r. Kalpana d. david Neels Ponkumar S. Siamala devi S. Jeya Lakshmi Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Cyber Security) Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study enhances secure data transmission by embedding sensitive information within audio files. Using Python for the frontend and PHP with MySQL as the backend, this approach employs reversible data hiding (rdH) to ensure that the original content of the audio file can be fully restored. Audio steganography is used to conceal encrypted messages within a WAV file, leveraging its varying compression rates for optimal steganographic performance. The Least Significant Bit (LSB) technique, a widely used method, embeds the hidden message into the audio stream without noticeable distortion. In this system, the secret message is scrambled, divided into segments, and embedded in even-value bytes of the audio file, with the first bit position chosen randomly for enhanced security. A permutation codebook is employed to rearrange and decrypt the message during recovery, ensuring both data confidentiality and complete reversibility of the audio file. This method provides a secure, efficient solution for transmitting sensitive information through audio channels.

关键词： Steganography data privacy distortion Encryption data communication Informatics Mobile computing

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Smart Food Calorie Prediction: Hybrid CNN-ML Model for Accurate Meal Tracking

Smart Food Calorie Prediction: Hybrid CNN-ML Model for Accur...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： K. P. Senthilkumar S. Thenappan Madhumitha J r.Alarmelu Mangai rajkumar Chadge Khaled Al-Qawasmi Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of EEE Bannari Amman Institute of Technology Sathyamangalam Erode Tamil Nadu India Department of Computer Applications Dr SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The food calorie prediction systems depend on image recognition, basic Machine Learning (ML) models, and manual input without adequate portion size analysis. It is well-recognized that these algorithms frequently identify food categories, portion sizes, and calorie counts incorrectly. The study introduces a unique hybrid model for automated meal tracking that uses Convolutional Neural Networks (CNN), machine learning, and visual feature extraction to achieve improved accuracy in calorie prediction, portion size estimation, and food recognition. It calculates the real calories by combining them with a nutritional database and using depth estimates to establish portion sizes. The results show that the proposed system outperforms the existing system in terms of accuracy (92.4%), precision (91.3%), and recall (93.2%) while requiring less time (1.10s). These demonstrate that a real-world system may be implemented to provide a scalable and well-recognized tool for diet tracking and health management

关键词： Visualization Accuracy Scalability Machine learning Predictive models Feature extraction User experience robustness Convolutional neural networks Visual databases

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Estimating Smart Grid Stability with Hybrid rNN+LSTM deep Learning Approach

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Saadin Oyucu Şeref Sağıroğlu Ahmet Aksöz Emre Biçer Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Gazi University Artificial Intelligence and Big Data Analytics Security R&D Center Ankara Türkiye Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing recurrent Neural Networks (rNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of rNN to recognize relationships between consecutive data points with LSTM’s capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.

关键词： deep learning data privacy recurrent neural networks Power system stability Stability analysis data models Smart grids

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