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9th International Conference on Biosignals, Images, and Instrumentation, ICBSII 2023

作者： Pancholi, Utkarsh dave, Vijay Gujarat Technological University L.D. College of Engineering Biomedical Engineering Department Gujarat Ahmedabad380015 India Gujarat Technological University Government Engineering College Sector-28 Biomedical Engineering Department Gujarat Gandhinagar382028 India

ISBN: (纸本)9798350338171

Transcranial direct current stimulation (tdCS) is a part of the transcranial electrical stimulation method widely used for treating patients with neurological and psychological abnormalities, along with application in cognitive improvements. With a simple design and operating procedure, tdCS is considered a safe and effective therapy choice. With predefined treatment protocols, it is possible to achieve the required electric field within the inner structures of the brain to excite and inhibit neuronal activity and its outcomes. The generated electric field shows variation among individuals due to anatomical and functional changes in the brain tissues. In-situ modeling of therapeutic procedures can help to assure the probabilistic outcome of tdCS. In this study, we have obtained results for electric field strength variability in a cognitively normal subject. We have simulated the subject with variation in stimulating electrode size and shape, a combination of electrode-Gel and electrode-sponge with SimNIBS Ver (3.2.6), and measured electric field strength and focality. Simulated results show less dependence on gel or sponge thickness and more reliance on electrode size and shape for E-field and focality. The increasing size of electrodes reduces electric field strength and focality with asymmetrical E-field distribution, whereas decrement generates a more symmetrical and focused E-field with higher strength. © 2023 IEEE.

关键词： Electrodes

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Anisotropy diffusion Kuwahara filtering and dual-discriminator d2C Conditional Generative Adversarial Network Classification on Spatio-Temporal Transportation's Traffic images 2

Anisotropy Diffusion Kuwahara filtering and Dual-discriminat...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gawali, Tukaram K. deore, Shailesh S. Government College of Engineering Computer Engineering Dept. Jalgaon India SSVPS's B. S. D. College of Engg. Comp. Engg. Dept. Dhule India

ISBN: (纸本)9798350387933

The Spatio-temporal transportations have various issues like traffic congestions, weather issues and wind directions. The major problems are to prevent from traffic-based accidents. The traffic may be in homogenous and heterogeneous format. In this paper the complete focus is based on heterogeneous traffic flow. As per the huge demands of vehicles either are petrol, diesel or electrical, the densities of vehicles are increases rapidly along with the population. India is the largest populated country in the world and having more vehicles as comparative roads availability. The whole world always faces issues to identify traffic condition with images. In our proposed system, the source image is extracted from a dataset of road vehicle images. To enhance the image quality, noise reduction is applied using Anisotropy diffusion Kuwahara filtering during the pre-processing stage. The classification using dCGAN-TdR is implemented to generate 97.74% accuracy with high precision. © 2024 IEEE.

关键词： Generative adversarial networks

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Feasibility Study on Municipal Solid Waste (MSW) as Sustainable engineering Material Using Suction Characteristics

Feasibility Study on Municipal Solid Waste (MSW) as Sustaina...

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Virtual Conference on disaster Risk Reduction , VCdRR 2021

作者： Shah, M.V. Brahmbhatt, A.J. L.D. College of Engineering Ahmedabad Gujarat India

ISBN: (纸本)9789811655425

For all major cities, municipal solid waste (MSW) landfills is a major concern regarding its impact on environment and the hazardous effect on water table below the MSW fill. Majority portion of the landfills above the ground surface remains unsaturated, and thus, suction is playing very much important role in terms of its stability and compressibility criterion. The Pirana MSW (Ahmedabad) material which used here is already more than 15-years old and achieved the height of more than 25 m. The study attempts to observe the suction characteristics of the waste for different moisture content and to establish the relation between suction parameters and strength of the waste. To check its suitability as the alternate material for construction purpose, the same study is carried out on silty sand so that the influence of the volatile matters present in the MSW on suction can be observed. To understand the behavior of compressed fill under the submergence condition and to measure suction, the MSW and silty sand samples were soaked for 24 h into water. The average pore size of the compressed mass is carried out using the relation of matric suction to pore size, and the void formation is observed in high-magnification microscope to visually check and co-relate the variation in pore size. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Municipal solid waste

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MultimodNet: A Multimodal deep learning Model for COPd Staging based on Chest X-Ray and Pulmonary Function Tests 3

MultimodNet: A Multimodal Deep Learning Model for COPD Stagi...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Krishnaveni, A. Rajkumar, G. Vinoth Raj, J. Relin Francis Krishnan, R. Santhana Murali, S. Settu, Imran Javeed Department of Mechanical Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronicsand Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798331532420

Chronic Obstructive Pulmonary disease (COPd) represents a major global health challenge, with early and accurate diagnosis being essential for improving patient outcomes. Traditional diagnostic approaches often rely on single-modality assessments, which may not fully capture the complexity of the disease. This research introduces MultimodNet, a deep learning framework that integrates both chest X-ray images and Pulmonary Function Test (PFT) data to provide a more comprehensive classification of COPd stages. By combining structural information from chest X-rays with functional lung metrics like FEV1, FVC, and the FEV1/FVC ratio, MultimodNet aims to enhance the accuracy and reliability of COPd diagnosis. The core strength of MultimodNet lies in its multimodal approach, which allows for the processing and fusion of diverse data types to overcome the limitations of using a single modality. This integration helps the model provide more accurate predictions regarding COPd progression, classifying patients into one of four stages: Initial, Progressive, Complicated, and Critical. These stages reflect the disease's clinical trajectory, from the onset of symptoms through to advanced stages where life-threatening complications may arise. By leveraging both imaging and clinical data, MultimodNet offers a powerful diagnostic tool that can assist healthcare providers in making early and precise decisions, leading to better treatment planning and improved patient care. This framework has the potential to not only enhance diagnostic workflows but also contribute to the broader field of AI-driven healthcare solutions, where multimodal data integration plays a key role in advancing disease detection and management. © 2024 IEEE.

关键词： Patient treatment

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designing an Intelligent data Transmission Strategy Between Entities Using Wireless Communication Methodology

Designing an Intelligent Data Transmission Strategy Between ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Geetha, R. Jayakumar, d. devi, S. Rukmani Senthurya, S. Akula, Akhila Karthiga, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Saveetha School of Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Chennai Department of Computer Science Tamil Nadu India Mlr Institute of Technology Department of Ece Hyderabad India R.M.K Engineering College Department of It Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

data security is of the utmost importance in wireless sensor networks due to the massive amounts of data collected and sent between nodes. An improved security method for wireless sensor networks is presented in this paper. This mechanism takes into account the application, the security level, and the bit error rate simultaneously. In order to provide the user with the ability to select between unsecure and secure modes, we made use of reserved bits of the frame control field that are contained within the communication header. The Secured and Intelligent data Transmission Protocol (SIdTP) is the name of the unified perceptual information transmission model that is established in this research. Its purpose is to compensate for the unequal distribution of cognitive radio users across different wireless lANs. For the purpose of determining whether or not the suggested scheme is effective, the proposed model will be cross-validated with the traditional data transmission model known as Wireless data Transmission Protocol (WdTP). The approach has the potential to optimize the utilization of spectrum resources in an efficient manner. In addition, to help lessen the risks connected with wireless networks, this study lays out a technique for transmitting data using cyclic delay full diversity sensing information coding. In order to evaluate the performance of the system against a variety of correlation coefficients, we developed a system simulation model. The result of the study showed that cyclic delay diversity systems had overall interference intensity similar to frequency division duplex as well as time division duplex. As an added bonus, this enhances its anti-jamming attack capabilities. The proposed system has been planned and implemented, and significant trials have been carried out in an industrial setting. Additionally, a prototype of the system has been designed. Compared to conventional communication interfaces that are only equipped with a single wireless interf

关键词： data transfer

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-dCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection deep learning Instagram Optimization Social network Twitter

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deep learning based Advanced Image Recognition in Autonomous Vehicles in Industry 4.0 3

Deep Learning based Advanced Image Recognition in Autonomous...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aruna, R. Manjula devi, P. Suresh, d. Arul Pandiyan, M. Sathya, S. Geetha, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vtu Affiliated Amc Engineering College Department of Information Science and Engineering Banglore India Unnamalai College of Arts and Science Department of Computer Science Kovilpatti India Bannari Amman Institute of Technolgy Department of Information Science and Engineering Erode India Bannari Kongu Engineering College Department of Mathematics Erode India Dean of Academics and Research Jct College of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350375190

The automation with integration of numerous devices are important factors in the manufacturing and production sectors in Industry 4.0. This involves the use of autonomous vehicles for diverse applications. They are based on advanced image recognition systems. The efficient functioning of the system is achieved using deep learning techniques. These networks have the ability to automatically learn from complex data. These deep learning techniques are used for the detection of obstacles, lane following and object recognition. The various constraints in obtaining the optimum performance are achieved using ant colony optimization techniques. This helps in obtaining the overall efficiency of autonomous operations. They helps in evaluating optimum solutions through iterative updation of the artificial pheromones. They are used for the optimization of hyperparameters and training parameters. They helps in fine tuning of the model for various requirements of autonomous vehicles in Industry 4.0. They results in reduced computational costs with improved convergence. The integration of deep learning with ACO helps in overcoming the challenges in the deployment of advanced image recognition systems in autonomous vehicles. They helps in improving the perception capabilities of the vehicles. These autonomous vehicles provide adaptive solutions for various processes in the manufacturing and production sectors. © 2024 IEEE.

关键词： Image recognition

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A Comprehensive Analysis of Cloud Computing in Wireless Communications 2

A Comprehensive Analysis of Cloud Computing in Wireless Comm...

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2nd International Conference on disruptive Technologies, ICdT 2024

作者： Bansal, Anurag Sharma, Surbhi Shukla, Ajay Narayan Badhoutiya, Arti Research and Technology 13D Research & Strategy India Abes Engineering College Department of Computer Applications UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

In the present-day digital landscape, cloud computing has grown to be a ubiquitous generation that offers numerous advantages to numerous businesses. Its software in wi-fi communications has had a mainly revolutionary effect, ultimately converting how data is transferred, considered, and saved. This looks at a thorough examination of how cloud computing has affected wireless communications, including its records, most important ideas, and programs. The primary phase of the object appears to be how cloud computing has changed over the years, moving from conventional on-premise computing to its current country, which includes a spread of service offerings and deployment methodologies. Subsequently, it explores the fundamental thoughts of cloud computing, which have virtualization, scalability, and on-demand provisioning, and talks about how these apply to wireless communications. The ability of cloud computing to grow network availability and dependability is one of its primary benefits for wi-fi communications. Using cloud-based computing and garage sources, network operators may additionally lower downtime and increase uptime for their services. They can also better manage unforeseen surges in community visitors and provide a higher user revel. The look additionally addresses how cloud computing helps the short rollout of the latest services and packages within the wireless communications industry. © 2024 IEEE.

关键词： Cloud Computing data Storage Mobile Technology Network Security Wireless Communications

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A Comprehensive Evaluaion of Solar Powered Electric Vehicle Charging Station design Using Internet of Things 1

A Comprehensive Evaluaion of Solar Powered Electric Vehicle ...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Chitra, K. Gnanasekar, A. Ravi, C.N. Jaisudha, J. Ramya, R. Jithendar, P. CMR Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India CMR Engineering College Department of Computer Science and Engineering Hyderabad Medchal India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India MLR Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350364590

More than 1.3 million electric vehicles have been installed throughout the globe, and public charging networks for these vehicles are becoming increasingly common. It has been an area of interest to establish a plan for prioritizing the charging of electric vehicles (EVs) from a single network. This is particularly important in situations when the power source is restricted, such as with renewable energy, and for economic reasons (a higher price per KWh for quicker charging). When used in conjunction with electric vehicle (EV) charging, solar energy significantly decreases our reliance on fossil fuels. An electric car solar charger is created and constructed inside this system. In order to maximize the output from the solar panels, a dc-dc boost converter is used to transfer the voltage from the panels to the station's batteries. Maximum Power Point Tracking (MPPT) is also included. Power the charging station with energy from the solar panels so that the vehicle's rechargeable battery may be charged. The charging station's status can be monitored at any time via the Internet of Things. We have created a solar-powered charging station to provide simple accessibility. Solar power is both a sustainable and accessible renewable energy source. Arduino controller for light-dependent resistor (ldR) light intensity monitoring, solar cell energy observation, battery charging, and cloud storage. The core idea behind the article is the IoT Solar Charging Station (IoTSCS), and it is tested by comparing it to the Classical Solar Charging Station (CSCS), a more traditional scheme, in order to see how well the new scheme works. © 2024 IEEE.

关键词： Charging stations

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (Ad) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise Ad diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (Ad) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PlS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-lMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-lMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-lMS technique is 96.23% and 95.82% for RSS and ANdI datasets respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, llC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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