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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Moyeenudin, H.M. Narender, M. Mukunthan, M.A. Raman, Kanimozhi Balasubramanian, D. Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai600117 India Tkr College of Engineering &technology Hyderabad India School of Computing Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai600062 India Kcg College of Technology Department of Mathematics Karapakkam Chennai600 097 India Jaya Engineering College Tamilnadu Thiruninravur602024 India

ISBN: (纸本)9798350322842

This article analyzes the power consumption of nodes in ad hoc IEEE 802.11 networks. The main objective of this analysis is to discover theoretic restrictions for the gains obtained in node lifetime through different energy conservation techniques found in the literature. The evaluation of energy consumption considers the characteristics of the medium access method and the interaction of the nodes in the packet forwarding process. The basic idea is to regulate the lifetime of a node based on its average consumption. Consumption is estimated based on the amount of time the node remnants transmitting, receiving or idle. The potential gains obtained by different techniques for saving or balancing energy consumption are then analysed. It is shown that the flow balancing technique can achieve a maximum gain for the node lifetime between 11% and 30%, depending on the network configuration. © 2023 IEEE.

关键词： Electric power utilization

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Machine Learning Based Cooperative Spectrum Sensing Using Regression Methods

Machine Learning Based Cooperative Spectrum Sensing Using Re...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Somula Lakshmikantha Reddy Meena M Electronics & Communication Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

New cooperative spectrum sensing (CSS), it encounters better spectrum efficiency, has come into existence as a new strategy. In this study, techniques based on machine learning (ML) work together with CSS to improve user understanding; this is only possible when ML algorithms predict channel states. Additionally, many popular regressions machine learning models such as linear regression, nonlinear regression, generalized linear model, regression tree, and support vector machine regression (SVM) ensembles are discussed along with statistical analysis. This article assumes that 20 main users have different power, controls the maximum power of 20 units, and fixes the threshold of 4 units for all four cases, and analyses the results. In this respect, we came to the conclusion that a best fit line should be used for obtaining characteristics such that linear regression could be used. To further enhance regression and achieve the best results, other changes were also employed.

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Design of AED Based Drone to Deliver the Medical Kit on an Emergency Basis 3

Design of AED Based Drone to Deliver the Medical Kit on an E...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Nagarani, P. Ahmad, Mohd Wazih Shalini, R. Singh, Pavitar Parkash Cvr College of Engineering Department of Emerging Technologies Hyderabad India Shri Ram Murti Smarak College of Engineering and Technology Department of Computer Science and Engineering Bareilly India Vels Institute of Science Technology and Advanced Studies Department of Bca & It School of Computing Sciences Chennai India Lovely Professional University Department of Management Phagwara India

ISBN: (纸本)9798350330724

Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes are now the only options for transporting medical supplies at times of urgent need, which may be expensive, time-consuming, and often impossible if the emergency site is far. Drones are a game-changer in the medical industry because they can transfer vital supplies like blood and medical equipment. Unfortunately, given the state of the art, the drone's battery life is a constraint that may slow down the delivery process. This article details the research into the mechanisms designed for drones to increase their battery life and efficiently deliver first aid supplies. The drone's system safely delivers and drops medical supplies, reducing the drone's takeoffs and landings. The primary goal of this research is to develop a device that can safely transport medical equipment to its destination and then carefully unload it there. Results showed the system could safely transport and deploy first aid supplies during flight and landing. © 2023 IEEE.

关键词： Drones

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Hybrid Cuk-Zeta Converter with Neuro Fuzzy Approach for EV Applications

Hybrid Cuk-Zeta Converter with Neuro Fuzzy Approach for EV A...

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International Conference on Smart Structures and Systems (ICSSS)

作者： Indirakumar. A N. Janaki Department of Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies Chennai India

The transportation sector is a key component of tackling global climate change, which facilitates sustainable development. An effective and environmentally friendly way to decarbonize the transportation industry is through electric vehicles (EVs). This paper proposes EV charging station, using hybrid converter technique along with Maximum Power Point Tracking (MPPT) approach. In this work charging of EV is accomplished by Photovoltaic (PV) system. The voltage from PV system is boosted with the support of Cuk-Zeta converter. Furthermore, Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT approach is established to control the converter along with maximum extraction of PV voltage. Finally, the voltage across DC link is transferred to EV battery from where, it is offered for Vehicles for charging. The exhaustive construction is examined and validated using MATLAB, proving that the proposed setup results in improved performance with 95.32% converter efficiency respectively.

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A Novel Spectrum Handoff Technique for Long Range Applications using Adaptive Beam Selection with Machine Learning Algorithms

A Novel Spectrum Handoff Technique for Long Range Applicatio...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Patan Babjan V. Rajendran Department of Electronics and Communication Engineering VELS Institute of Science Technology and Advanced Studies Chennai India

A cognitive radio network is an intelligent system that can detect the presence of unused spectrum space without affecting the primary user. The CRN is responsible for managing the allocation of channels, which leads to the scarcity of spectrum. This issue should be addressed in order to ensure that the network can continue to provide long-term and profitable service to its users. The issue of spectrum handoff is considered a major issue that needs to be resolved in order to improve the efficiency of the network. One of the most common factors that can affect the network's performance is the power consumption and communication delay. This paper proposes a method that can detect the availability of channels during the handover. The accuracy and network latency of various ML algorithms are evaluated through resampling techniques. The Nave Bayes Classifier and KNN algorithms performed better than their benchmarks. For a total of 500 and 100 users, respectively, the networks experienced a network latency of 10.91 and 13.08 seconds.

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UGVs for Agri Spray with AI assisted Paddy Crop disease Identification 3

UGVs for Agri Spray with AI assisted Paddy Crop disease Iden...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Sujatha, K. Reddy, T. Kalpalatha Bhavani, N.P.G. Ponmagal, R.S. Srividhya, V. Janaki, N. EEE\ECE Department Dr. MGR Educational and Research Institute Chennai600095 India ECE Department Saveetha Institute of Medical Sciences Chennai India CSE Department SRM Institute of Science and Technology Kattankolathur India EEE Department Meenakshi College of Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Chennai India

This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in "All terrain conditions". They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on "All terrain traversability" and "dead zone" monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB

关键词： Controllers

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Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN

Named Entity Recognition for Protecting Sensitive Data using...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： Sheela Gowr. P Kumar. N Department of Computer science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall.

关键词： Deep learning Training Radio frequency Databases Data security Forestry Predictive models

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Interactive Sign Language Learning System Using Computer Vision and Deep Learning

Interactive Sign Language Learning System Using Computer Vis...

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

作者： Suganiya Murugan Mir Kasif Ali Dhanvanth Singari S. Pradeep Kumar Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In an era where communication is key, the gap in accessible tools for those with hearing impairments or speech disabilities is significant. These individuals often face obstacles in education and social interaction due to a heavy reliance on spoken language and a lack of sign language resources. The Interactive Sign Language Learning System (ISLLS) addresses this gap by providing an innovative platform for learning sign language, enhanced with voice output to assist individuals with speech disabilities. This feature allows for auditory feedback alongside visual sign learning, enriching the educational experience. The ISLLS employs advanced technologies like computer vision and deep learning to facilitate sign recognition and text-to-sign conversion. With the new voice output, it further aids those with speech impairments, expanding its inclusivity. This system offers a comprehensive learning tool that caters to a diverse user base, enabling people with speech difficulties to engage more fully with the world. The ISLLS is a significant step towards a more inclusive society, offering a user-friendly platform that not only improves the learning of sign language but also empowers people with speech disabilities to connect and thrive, representing progress in both technology and social inclusivity.

关键词： Learning systems Deep learning Sign language Computer vision Visualization Transforms Speech recognition

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Improvement of Interference Fringes Analysis to Obtain Accurate Soret Coefficients

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Defect and Diffusion Forum 2025年 439卷 291-304页

作者： Horikoshi, Haruki Orikasa, Isamu Kataoka, Minami Inatomi, Yuko Suzuki, Shinsuke Department of Applied Mechanics and Aerospace Engineering Waseda University Tokyo169-8555 Japan Department of Materials Science Waseda University Tokyo169-8555 Japan Kagami Memorial Research Institute of Materials Science and Technology Waseda University Tokyo169-8555 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Space and Astronautical Science The Graduate University for Advanced Studies SOKENDAI Kanagawa252-5210 Japan

In this study, we proposed a moved-view analysis, a method for obtaining the Soret coefficient ST by analyzing the interference fringe change during field-of-view movement in the steady state. This analysis was designed to solve the problem that the sign of the concentration gradient could also be reversed if the plot of the concentration distribution was only slightly shifted owing to the narrow field of view. The data obtained from the experiment conducted to measure ST at the International Space Station were analyzed using moved-view analysis. For the moved-view analysis, the linearity of the concentration distribution induced by the Soret effect is larger than that for the method without field-of-view movement, and a more reliable ST can be obtained. The analysis error that sometimes occurred when the laser wavelength switched led to the underestimation of the phase change Δ, resulting in generating the data with low linearity. This unreliable data should be removed. © 2025 Trans Tech Publications Ltd, All Rights Reserved.

关键词： Interferometers

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Deep Learning-Based Alzheimer's Disease Classification using Transfer Learning and Data Augmentation

Deep Learning-Based Alzheimer's Disease Classification using...

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Intelligent Technologies (CONIT), International Conference on

作者： Deva Anand P. Sheela Gowr S. Logesh S. M. Anand Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise for AD classification. This research study explores the effectiveness of transfer learning in training models based on deep learning for the classification of Alzheimer's condition. We trained several transfer models using DenseNet121, InceptionV3, Xception, and ResNet101. Based on performance evaluation, InceptionV3 was selected as the base model, outperforming in comparison to the accuracy of the other models. To enhance the accuracy of the InceptionV3 base model, we added capacity, tuned hyperparameters using Keras tuner, and utilized data augmentation techniques. The final model was trained on the Kaggle Alzheimer's Dataset, consisting of 4 classes of images, and achieving an AUC value of 87%. Our research demonstrates that transfer learning and other data augmentation approaches are useful in improving the accuracy of deep learning models for the categorization of Alzheimer's condition. This research has practical implications for improving public health outcomes, facilitating timely intervention and effective treatment. It contributes to the development of more accurate diagnostic tools for AD and can help address the challenges associated with this disease.

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