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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Ezilarasan, M.r. Gnanaprakash, V. Sreelakshmy, r. Preethi, d. rajesh, G. rahul, S.G. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331528539

Water supplies are essential to farming and agriculture productivity as well as to our standard of living. Traditional water supply and quality monitoring systems need a large workforce in addition to complicated infrastructure. Agriculture depends heavily on the ability to control the level of water of a water supply, such as a water tank, bore well, etc It is difficult to monitor water tanks in real time using traditional water distribution methods. We can adjust to the newest technology in order to remove the complexity and streamline the system. For the water level monitoring system, a PId Control scheme that is solely software-oriented is thus created using *** Internet of Things (IoT) concept is used for continuous monitoring. In addition to tracking usage and gathering data via Wi-Fi and LAN, the server also keeps an eye on how much water is wasted at each outlet due to overuse. An alert is mailed to the user to let them know when water usage exceeds a predetermined level. With the help of this, customers can utilise a laptop or mobile device with an internet connection to continuously monitor water levels To prevent hacking, it additionally offers verified user access to this system. The government and authorities may monitor and manage the water supply with the help of this suggested water level monitoring system, which also enables individuals, schools, hospitals, businesses, department stores, malls, and agricultural areas to keep track of how much water they waste. © 2024 IEEE.

关键词： Water tanks

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AI Based Skin Cancer diagnosis 2

AI Based Skin Cancer Diagnosis

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2nd IEEE International Conference on Emerging research in Computational science, ICErCS 2024

作者： rajalakshmi, N.r. Sankar, Pasala Mani raju, Kosireddy Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai63 India

ISBN: (纸本)9798331534950

This study presents an innovative approach to skin cancer detection using Artificial Intelligence and deep learning methodologies. The AI model, trained using Convolutional Neural Networks (CNNs), outperformed human diagnostic abilities in distinguishing between cancerous and healthy skin, achieving an accuracy rate of over 90% in detecting skin cancer. The model also demonstrated proficiency in classifying various types of skin cancers, offering expedited and precise diagnoses. This breakthrough in skin cancer diagnosis has the potential to revolutionize early detection and alleviate strain on healthcare systems, enabling individuals to screen for skin cancer without a doctor's visit. The designed AI-powered deep convolutional neural network system capable of automatically analyzing and classifying skin lesions based on dermoscopic images. By harnessing the power of deep learning and CNNs, it endeavors to achieve heightened accuracy and efficiency in skin cancer detection. © 2024 IEEE.

关键词： Lung cancer

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Webcam painting canvas window application using OpenCV and Numpy

Webcam painting canvas window application using OpenCV and N...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Usha, V. Arivubrakan, P. Sai Balaji, K. CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9781032669663

designers all understood quite well that the most popular and efficient means of expression for our emotions are via art and writing. In the olden days, people used to write or print on the rocks, walls, stones, but as time passed the process changed. People had discovered new plants and extracted the minerals and they turned it into papers. Then they created different kinds of artificial papers. In this modern area, this has gone too far. People are using electronic devices like screens, where we can write, draw and represent various structures, symbols, arts, painting, etc. either by hand, by typing and also just with a single tap. But now, we are taking this process more far, making it easier to use and easier to express your feeling just by moving your little fingers. We know canvas is a special type of surface where it is also used for painting. And now imagine a surface where there is no physical object and try to paint or write anything. Is it possible? Yes, this is possible using air canvas. Air canvas is a digital drawing which uses free-hand. All you have to do is to think about what you want to express or show, and then just move your finger according to what you wanted to express. © 2024, CrC Press/Balkema. All rights reserved.

关键词： Thermoelectric equipment

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Systematic Analysis of Historical Analogies and Challenges: Lung Cancer detection 5

Systematic Analysis of Historical Analogies and Challenges: ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathyamoorthy, K. ravikumar, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai Avadi India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Cancer is a fatal condition that usually comes on by the aggregation of several diseases and inherited illnesses. Wherever in the human body, cancerous cells can develop and cause deadly growths. Cancer, referred to as a tumor, needs to be immediately and precisely identified in its earliest phases in order to decide what can be effective for its treatment. Even while each method has its own distinct problems, some of the main reasons for death include challenging histories, poor diagnosis, and subpar treatments. The goal of the study is to evaluate, classify, examine, and discuss recent developments in the detection of cancer in lung using different learning techniques. The main objective of this work is to study and compare the different modalities like machine learning using unsupervised, supervised, as well as deep learning algorithms that can help in lung cancer detection and therapy. The findings are compared using standard datasets for metrics including precision, specificity, sensitivity, and accuracy. Several novel strategies are clustered under one cluster. Last but not least, challenges are also emphasized for prospective future work. © 2024 IEEE.

关键词： Lung cancer

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An Automated deep Learning-based VAr for Enhancing referee decision-Making in Football 3

An Automated Deep Learning-based VAR for Enhancing Referee D...

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3rd International Conference on Intelligent data Communication technologies and Internet of Things, IdCIoT 2025

作者： Zaken, Hamza Abu Prasanth, A. Jahjah, Mohamad Ali Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant referee (VAr) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAr system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAr methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating. © 2025 IEEE.

关键词： Convolutional neural networks

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Influence of silica fume and metakaolin on durability properties of hybrid fibers reinforced high strength concrete

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Journal of Building Pathology and rehabilitation 2023年第1期8卷 1-12页

作者： Sriram, M. Sidhaarth, K. r. Aswin Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

Abstract: The durability properties of hybrid fiber-reinforced high-strength concrete (HFrHSC) were examined in this research. Silica fume and metakaolin were added to both HFrHSC and conventional concrete specimens. ramie fiber usage in concrete is a new trend and it gives good results compared to other types of natural fibers. The optimum content of silica fume is 10% and metakaolin is 5% were used as cementitious composites in the hybrid fiber reinforced concrete (HFrC) mixes. A concrete mix of grade M₇₀ was used for this investigation. For developing HFrHSC, different proportions of ramie and steel fibers were mixed with cementitious composites. ramie fiber fractions of 0.5%, 1%, 1.5%, and 2% were kept constant with varying fractions of hooked-end steel fibers of 17 different mixes were prepared. The durability properties of HFrHSC were determined by different tests such as water absorption, acid test, sorptivity test, drying test, sulphate test and marine attack test. The water absorption of HFrHSC was decreased by 4.5%, Sorptivity was decreased by 3.5%, drying shrinkage was decreased by 3%, and the results of acid test of HFrHSC was decreased by 10%, Sulphate attack test of HFrHSC was decreased by 12% and marine attack test results 11% compared to the conventional concrete specimens. The hybrid fiber reinforced concrete specimens exhibits highly reduced absorption capacity than conventional concrete specimens. The effects of HFrHSC in acid tests shows less deteriorations compared to the conventional concrete specimens. Graphical abstract: [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Cementitious composites durability properties Marine attack test Natural fiber

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OCCOA for clustering-based intrusion detection system with MLP-rNN architecture

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Multimedia Tools and Applications 2025年第11期84卷 8567-8592页

作者： Kagade, ranjeet B. Vijayaraj, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Chennai600 062 India

WSN play a critical role in various applications such as environmental monitoring, surveillance, and healthcare. However, their deployment in open and dynamic environments makes them vulnerable to security threats. IdS are essential for safeguarding WSNs against malicious attacks and ensuring the integrity of data transmission. This research introduces a novel Clustering-based Intrusion detection System (CIdS) tailored for WSNs. The CIdS framework encompasses two primary components: optimal CH selection and intrusion detection within the WSN. Clustering is executed based on specific constraints such as energy, distance, rSSI, delay, and Pdr. The selection of optimal CHs is facilitated by the hybrid optimization approach called OCCOA, which combines Osprey optimization algorithm (OOA) and Coot Optimization Algorithm (COA). Following CH selection, intrusion detection is performed within the WSN using dL strategy. The CIdS employs a Multilayer Perceptron-recurrent Neural Network (MLP-rNN) model to detect intrusions by assessing trust and security. Additionally, the weights of the MLP-rNN model are finely tuned through the OCCOA algorithm for optimal performance. Moreover, the proposed method achieved higher performance by considering the measures, namely accuracy, sensitivity, precision and specificity with the values of 98.25%, 99.96%, 99.37% and 97.52%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Optimized Fuzzy Logic Adaptive System with Holographic Convolutional Heterogeneous Graph Neural Network-based Feature Extraction and Classification for Brain Tumor detection 8

Optimized Fuzzy Logic Adaptive System with Holographic Convo...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： regan, M. Srinivasan, P. Santhosh Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Cells growing abnormally in the brain or surrounding tissues is called a brain tumor. It can cause symptoms such as headaches, seizures, and physical or cognitive deficits. It can also be benign (non-cancerous) or malignant (cancerous). To control its effects and enhance results, early identification and treatment are essential. It is necessary to detect BC, because of that many techniques are implemented. But the existing methods have lot of disadvantages such as, overfitting high computational complexity and resource demands. To overcome the aforementioned problem, Holographic Convolutional Neural Network with Heterogeneous Graph Neural Networks using Boosted Sooty Tern Optimization Algorithm (HCNN-HGNN-BSTOA) is proposed for accurately detecting BC.. In this input image is taken from Figshare dataset and Brats2020 *** pre-processing and feature extraction are done using Filter based Fuzzy Logic Adaptive System Based Feature Extraction (CFFLAS) is proposed, for the purpose of removing noises and improve the efficiency of feature extraction. Following that future selection are done using Fennec Fox Optimization Algorithm (FFOA). After that classification are done using Holographic Convolutional Neural Network (HCNN)and optimization are done using Boosted Sooty Tern Optimization Algorithm (BSTOA) for detecting normal and abnormal conditions of the BT. The efficiency of the proposed HCNN-HGNN-BSTOA is analyzed using an dataset and attains 99.78% accuracy, 97.34% recall and attains better results compared with the existing methods. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Parasitic Mushroom-Like Structure with High Gain Microstrip Patch Antenna for Broadband Applications

A Novel Parasitic Mushroom-Like Structure with High Gain Mic...

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International Conference on Machine Learning and Big data Analytics, ICMLBdA 2023

作者： Sahaya Sheela, M. Syam Sudheer Babu, G. Sai durga Prasad, S.N.V. Vasanth Kumar, M.d. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of science and Technology Chennai India

ISBN: (纸本)9783031513374

In this research, a design for a microstrip patch antenna loaded with a parasitic mushroom-like structure is proposed. Antenna components include a rectangular microstrip patch as the main radiating element and one or more metallic patches as parasitic elements, separated by a dielectric layer. The parasitic patches are arranged in a pattern that resembles a mushroom growth, and their dimensions are optimized to improve the antenna’s performance. According to simulation results, the suggested antenna has a bandwidth of up to 20% and a high gain of up to 10dBi, which is much broader than that of a typical microstrip patch antenna. The radiation characteristics of the antenna are also improved, with a more uniform radiation pattern and higher front-to-back ratio. The suggested antenna design is appropriate for a variety of uses, including wireless communications, radar systems, and satellite communications, which call for high gain and broad coverage. The simplicity and low profile of the antenna make it easy to manufacture and integrate into various systems. Overall, the proposed parasitic mushroom-shaped microstrip patch antenna, which has broadband and high gain, is a promising solution for achieving improved antenna performance without increasing the antenna’s size or complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Slot antennas

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Crop Yield Prediction Using Machine Learning techniques Based on Soil and Environment Traits - A Survey 2

Crop Yield Prediction Using Machine Learning Techniques Base...

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2nd International Conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Mariammal, G. Sridevi, S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350328202

In research, the examination of agriculture is increasing rapidly. Crop yield prediction is an important aspect in agriculture. In earlier days, farmers get good yielding. due to the inefficiency in soil nutrients level and climatic changes crop yield level decreasing day by day. Hence, estimating the production is a crucial role in agriculture area also it helps to farmer for storing and marketing their yielded products. For predicting the precise agricultural yield, a variety of techniques, including remote sensing and machine learning, are used. This paper discussed a number of strategies for predicting crop yields that had been used by various researchers. In accordance to this review, the deployment of machine learning in agriculture indicates more effective and precise farming with fewer human assistance, as well as higher quality goods. © 2024 IEEE.

关键词： remote sensing

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