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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Saravanan, T. Ramesh, Gajula Hussein, Ramy Riad Bansal, Saloni Kumar, Y. Lakshman Krishnakumar, K. New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GRIET Bachupally Department of CSE Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GLA University Department of Electronics & Communication Engineering Mathura India Department of ECE CMR Technical Campus Telangana Hyderabad India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

This research examines the possibility of smart contracts in the blockchain concerning the improvement of sustainable supply chain management. Blockchain is a practical solution for increasing supply chain transparency and solving the most important problems, including the use of traceability for fraud prevention and compliance. The study uses a consecutive approach which involves case studies, analytical work, and simulation to assess the effects of blockchain on sustainability dimensions. As highlighted in the findings above, there were significant reductions in the time taken to achieve traceability, in fraud, as well as an improvement in ethical sourcing compliance. If applied, smart contracts strategically anchored on the blockchain technology can be deemed as a sustainable solution to supply chain issues. To build on the existing knowledge, further studies should investigate strategies used to mitigate failure threats that hinder the reaping of benefits from the technology. © 2024 IEEE.

关键词： Smart contract

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Assistive Mobile Robot Navigation and Localization using FastSLAM Algorithm 5

Assistive Mobile Robot Navigation and Localization using Fas...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Sai Lingesh, R. Annapoorani, G. Srm Institute of Science & Technology Ramapuram Department of Electronics and Computer Specialization Engineering Chennai India Srm Institute of Science & Technology Ramapuram Department of Ece Chennai India

ISBN: (纸本)9798350321425

This study introduces a novel technique to improve a mobile robot's ability to move and navigate on its own in uncharted territory. Robots can navigate a normal human workstation on their own thanks to simultaneous localization and mapping technologies. The SLAM technique is mostly used for mapping and localization in static environments. In this study, a special FastSLAM algorithm augmentation that includes a module for tracking individuals using 2D Lidar sensor data is designed and evaluated. The SLAM algorithm may filter measurements from those measures because of the noise generated by people walking is unstable and dynamic by nature. This has been tested with our mobile robot platform for social support. This approach has a more accurate localization and a clearer depiction of the mapping of the environment. Overall, the range of mobile assistive robots is broad and is expanding as new uses are found and technology develops. These robots have the potential to make a big influence on a lot of different businesses and enhance a lot of different elements of daily life. © 2023 IEEE.

关键词： Mobile robots

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Unveiling House Price with Machine Learning Algorithm

Unveiling House Price with Machine Learning Algorithm

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kusuma, Rambabu Kumar, Kalagadda Kiran Pillutla, Siva Ramakrishna Saikumar, Aruru Vignan's Foundation For Science Technology and Research Department of Computer Science and Engineering Andhra pradesh Guntur India School of Electronics Engineering VIT-AP University Amaravati522241 India Vnr Vjiet Dept of Ece Bachupally India

ISBN: (纸本)9798331509828

A frequently overlooked and yet very critical factor in having a high quality of life is the perfecting of one's living environment. A house should be quiet, unique, and instill peace in the soul of its residents, attaining individual needs across many aspects. Therefore, finding such a house with great architecture and making the right choice is essential. Beyond location, the structure and cost of a house are key elements that significantly influence the suitability of a place to live. Accurate price predictions are crucial to address potential future concerns. This study presents a model designed to help individuals select an ideal home based on their financial circumstances. It evaluates various machine learning models, including the Random Forest Regressor and Gradient Boosting Regressor, to ensure reliable predictions. The proposed model aims to present estimates as closely as possible aligned with ideal conditions, considering individual preferences and demands. © 2025 IEEE.

关键词： Adversarial machine learning

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Modeling of metaheuristic-based dual cluster head selection with routing protocol for energy-efficient wireless sensor networks

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Soft Computing 2025年第6期29卷 2999-3020页

作者： Reddy, Maddikera Krishna Veluswamy, Anusha Sowbarnika Selvanayaki, S. Harini, C. Kumar, Pavan Shameem, Syed Department of Electronics and Communication Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyal India Department of Mathematics and Computer Science Susquehanna University Pennsylvania SelinsgrovePA United States Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Maisamma Guda Secunderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Dakamarri Vizayanagaram India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Wireless Sensor Networks (WSN) are decentralized networks of spatially distributed sensors that gather and monitor environmental information for various applications, including industrial automation and environmental monitoring. This enables real-time information gathering without relying upon wired connections. In WSN, clustering and routing are essential techniques for improving data transmission. Clustering comprises grouping sensor nodes (SN) into clusters to optimize energy efficacy and extend the network's lifetime. However, selecting suitable and efficient cluster head (CH) components is crucial to harness their benefits. Selecting appropriate CHs and finding optimum coefficients for all the parameters of applicable fitness function (FF) in CH selection is a non-deterministic polynomial-time (NP-hard) problem that needs additional processing. In contrast, routing focuses on defining the most effective path for data to traverse through the network, ensuring timely and reliable data delivery and reducing energy consumption. Therefore, this study proposes a new Metaheuristic-Based Dual Cluster Head Selection with Routing Protocol (MBDCHS-RP) method for energy-efficient WSNs. The MBDCHS-RP method aims to cluster the nodes and select optimum routes for transmitting information in the WSN. To achieve this, the MBDCHS-RP method follows a two-stage clustering process, including tentative CH (TCH) and final CH (FCH) selection. The MBDCHS-RP technique utilizes a cheetah optimization algorithm (COA) with FF comprising residual energy (RE) and average node distance as input parameters for the TCH selection process. In addition, the flower pollination algorithm (FPA) is used to select final CHs with multiple input parameters, namely average intra-cluster distance, RE, and average sink distance. A carnivorous plant algorithm (CPA) is employed for the optimal route selection process with two parameters, RE and distance to the base station, to enable an effective data transm

关键词： Sensor nodes

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Sparse Autoencoder with SGReLU Activation Based Network Intrusion Detection System

Sparse Autoencoder with SGReLU Activation Based Network Intr...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Chandra Sekar, P. Kumar, Komuravelly Sudheer Bindu, Garikapati Kumar, R. Dinesh Pradeep Kumar, S. Siddartha Institute of Science and Tech Department of Ece Puttur India School of Computer Science & Artificial Intelligence Sr University Warangal India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India Saveetha School of Engineering Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

A Network Intrusion Detection System (NIDS) is the most important function, which is utilized to monitor malevolent activities frequently and detect these security attacks in Internet of Things (IoT) network. An improvement of effective performance and security is the most needed in the IDS system. This Sparse Autoencoder with Self-Gated Rectified Linear Unit (SGReLU) activation model for intrusion detection systems. The proposed Sparse Autoencoder with SGReLU activation function is evaluated using CIC-IDS-2017 and NSL-KDD dataset. The CIC-IDS-2017 and NSL-KDD dataset is standardized using Label encoder and one hot encoder method which improves the model performance. The pre-processed features are selected by using second percentile method and recursive feature elimination algorithms and the selected features are classified by using Sparse Autoencoder with SGReLU activation function which provides significant results. The proposed Sparse Autoencoder with SGReLU activation function model provides better results by utilizing evaluation metrics like accuracy, precision, f1-score values and PAR about 99.99%, 99.99%, 99.99% and 0.06 by using CIC-IDS-2017 dataset respectively when compared to the LSTM-CNN, NSOM, statistical method and LSTM-AE. © 2023 IEEE.

关键词： Internet of Things Label encoder and one hot encoder Network Intrusion Detection Self-Gated Rectified Linear Unit and Sparse Autoencoder

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Deep Learning-Based Approaches for Enhanced Image Compression in computer Vision Applications

Deep Learning-Based Approaches for Enhanced Image Compressio...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Avinash, Avinash Quevedo Lezama, Christian Rafael Murugesan, Ramya Anitha, K. Bharathi, C.R. Abid, Y. St. Josephs College of Engineering Department of Mba Chennai India National University of San Marcos Department of Electrical and Electronics Engineer Lima Peru Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Coimbatore India Saveetha Engineering College Thandalam Department of Ece Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha University Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350375442

Image compression is required for many computer vision applications, but traditional methods like JPEG and PNG have trouble preserving image quality at high compression levels. The study suggests a deep learning-based method for efficiently compressing images while maintaining visual information, utilizing convolutional neural networks (CNNs). Preparing the dataset, choosing CNN architectures (such as autoencoders and GANs), training with loss functions like MSE, and assessing using PSNR and SSIM metrics are all part of the process. According to the results, the deep learning approach outperforms JPEG (18:1) and PNG (6:1) in terms of compression ratios for complicated textures, reaching up to 25:1. The deep learning approach's PSNR values are 35.2 dB, while its SSIM scores are 0.92 in contrast to JPEG's (0.87) and PNG's (0.84) values. The process preserves visual quality while offering increased storage and transmission efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Techniques for Fault Detection in Industrial Machinery 5

Deep Learning Techniques for Fault Detection in Industrial M...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Thiyagarajan, T. Tamilselvi, M. Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Nehru Institute of Information Technology and Management Department of Computer Applications Tamilnadu Coimbatore India Roever Engineering College Computer Science And Engineering Tamilnadu Perambalur India Aditya Engineering College Department of ECE India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. These models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For the purpose of this , the performance of various methodologies is evaluated over a wide range of industrial situations, taking into consideration issues such as the variability of sensor data and noise. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient. © 2024 IEEE.

关键词： Condition monitoring

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Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood electronics 2023年第2期1卷 145-155页

作者： Donapati, Ramesh Reddy Cheruku, Ramalingaswamy Kodali, Prakash National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal506004 India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Dept. of Ece Hyderabad500090 India National Institute of Technology Warangal Department of Computer Science and Engineering Warangal506004 India

Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency. © 2023 IEEE.

关键词： Crops

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An IoT-Based Deep Learning Approach for Online Fault Detection Against Cyber-Attacks

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SN computer Science 2023年第4期4卷 393页

作者： Rajkumar, Sujatha Sheeba, S. Lilly Sivakami, R. Prabu, S. Selvarani, A. Department of Embedded Technology School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Department of ECE Mahendra Institute of Technology Tamil Nadu Namakkal India Department of ECE Panimalar Engineering College Tamil Nadu Chennai India

Trustworthy has been included as proof of the passed-in quality, which is safety, security, and privacy, and is not time limited. A real-time system operates according to a set of expected behaviors and conditions. Internet of Things (IoT) systems and applications are essential industrial investments expected to be of critical importance. Maintaining the reliability ofq7 such systems and networks is expensive, time-consuming and costly. A reliable IoT system considers the system operation’s security features and IoT reliability. These difficulties incorporate information breaks, phishing and spam crusades, and dispersed refusal of administration (DRoA) assaults, and malevolent exercises, for example, security breaks focusing on IoT gadgets. Deep learning (DL) strategies have been proposed to recognize pernicious traffic information, especially for malignant assaults against IoT gadgets. The proposed layered, profound learning strategy is stacked long short-term memory (SLSTM) coordinated with pre-prepared deep learning (DL) to gain proficiency with the attributes of dubious exercises inside and out and recognize them from ordinary traffic. Each pre-prepared DL model comprises the remaining blocks. We have utilized two huge datasets to evaluate the presentation of our discovery technique. Mixed IoT conditions guarantee that this approach can be applied to any IoT climate. Our proposed method, SLSTM, can recognize most IoT assaults by identifying harmless and malignant traffic information. The train results demonstrate that the proposed layered, profound learning technique can give a higher continuous location rate than existing grouping strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cyber-attack Deep learning (DL) Internet of Things (IoT) Malicious Neural networks Security Stacked long short-term memory (SLSTM)

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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization 2

Fault Classification in Photovoltaic Arrays Using Convolutio...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Pradeep Kumar, S. Umasankar, L. Almusawi, Muntather Surekha, Pothireddy Aluvala, Srinivas Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India The Islamic University Najaf Iraq Griet Department of Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382952

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods;and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems. © 2024 IEEE.

关键词： Convolutional neural networks

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