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IoT based system for monitoring and control of industrial process using real-time firebase database

AIP Conference Proceedings 2023年第1期2427卷

作者： G. Joselin Retna Kumar Khaldoun Zaki 1Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai TN India

This system is developed to achieve remote access to control and monitor an industrial process controlled using SIMATIC S7-1200 Siemens PLC which is a ’stamping machine’. Raspberry Pi has been used as a gateway to connect between the PLC and real-time Firebase database in the cloud. Also, a web page is developed to give the users the ability to interact remotely.

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Condition Generative Adversarial Network Deep Learning Model for Accurate Segmentation and Volumetric Measurement to Detect Mucormycosis from Lung CT Images

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Open Public Health Journal 2024年 17卷

作者： Chakrapani, Kannan Safa, M. Gururaj, H.L. Ravi, Vinayakumar Ravi, Pradeep Department of Information Technology School of Computing SASTRA Deemed University Tamil Nadu Thanjavur 613401 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Computer Science and Engineering GSSS Institute of Engineering and Technology for Women Karnataka Mysuru India

Introduction: Mucormycosis (black fungal attack) has recently been identified as a significant threat, specifically to patients who have recovered from coronavirus infection. This fungus enters the body through the nose and first infects the lungs but can affect other body parts, such as the eye and brain, resulting in vision loss and death. Early detection through lung CT scans is crucial for reliable treatment planning and management. Methods: To combat the above problems, this paper introduces a Condition Generative Adversarial Network Deep Learning Model (CGAN-DLM) to facilitate the automatic lung CT image segmentation process, contributing to accurately identifying Mucormycosis earlier. This deep learning model employed different pre-processing strategies over raw lung CT images for extracting its ground truth values based on potential morphological operations. It adopted CGAN to segment the region of interest used for diagnosing mucormycosis with the pre-processed images and their related truth values. Results: It also included a volumetric assessment approach that significantly identified the change in lung nodule size before and after the infection of mucormycosis. Conclusion: The extensive experiments of the proposed CGAN-DLM conducted using lung CT images taken from the LIDC-IDRI database confirmed sensitivity of 98.42%, specificity of 98.86% and dice coefficient index of 97.31%, on par with the benchmarked lung CT images-based Mucormycosis detection approaches. © 2024 The Author(s). Published by Bentham Open.

关键词： Condition generative adversarial network (CGAN) Dice coeficient index Lung CT images Lung nodule volumetric assessment Mucormycosis (black fungal attack) Potential morphological operations

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Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization

Model and Algorithm of Multimodal Transportation in Logistic...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： K. Babu K Subramani Ahmad Hussein Alawady Ahila R Inderpreet Kaur B. Gunapriya Department of Computational Intelligence SRM Institute of Science & Technology Kattankulathur Tamilnadu India School of Business and Management CHRIST (Deemed to be University) Bangalore Karnataka India College of Technical Engineering The Islamic University Najaf Iraq Computer Science and Engineering New Prince Shri Bhavani College Of Engineering And Technology Chennai Tamilnadu GCET Greater Noida Department of Electrical and Electronics Engineering New Horizon College of Engineering Bangalore

With the rapid improvement of market economy and modern logistics technique, the logistics distribution link is receiving more and more attention, and the logistics distribution path question in distribution has become the core question in logistics distribution. Study the optimization of logistics distribution path. Logistics distribution path optimization needs to find an optimal distribution route with less distribution vehicles and the shortest total length of the path, and has the rapidity of distribution. The traditional algorithm takes a long time to search the optimal route, which makes it difficult to find the optimal distribution route, resulting in high logistics distribution costs. In order to quickly find the optimal distribution route and improve the quality of logistics service, a logistics model based on particle swarm optimization algorithm is proposed. The group is composed of several non-intelligent individuals or groups of individuals. Each individual's behavior follows certain simple rules and has no intelligence; Individuals or groups of individuals can cooperate to solve questions through certain principles of message exchange, thus showing the behavioral characteristics of collective intelligence. After research, the algorithm in this paper is effective and suitable for wide application in practice.

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Action recognition using Correlation of Temporal Difference Frame (CTDF)—an algorithmic approach

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Journal of Ambient Intelligence and Humanized Computing 2020年第7期12卷 7107-7120页

作者： Poonkodi, M. Vadivu, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Information Technology SRM Institute of Science and Technology Chennai India

Presently in most of the real world applications like video surveillance systems, human activities are captured and retained as multimodal information for authorized permitted actions. However the degree of accuracy in recognition of such actions greatly depends on many factors, including occlusion, illumination factor, cluttered environment, and so on. In this work we propose the correlation of temporal difference frame (CTDF) algorithm which captures the local maxima’s of every small movement and its neighboring information. Temporal difference obtained between frames, block size defined to obtain the surround information and finally, the comparison of one to all points between identified frames greatly increase the accuracy. The algorithm takes in the raw video input of the standard UT interaction and BIT interaction datasets. Features extracted using the proposed algorithm is passed through variants of SVM which gives state of art results, 95.83% accuracy for UT Interaction and an accuracy of 90.4% for BIT interaction dataset.

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Towards developing an ensemble based two-level student classification model (ESCM) using advanced learning patterns and analytics

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Journal of Ambient Intelligence and Humanized Computing 2020年第7期12卷 7095-7105页

作者： Vidhya, R. Vadivu, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Information Technology SRM Institute of Science and Technology Chennai India

In recent decade, learning analytics has gained more attention and several advanced data mining models are developed for deriving the hidden sources from educational databases. The extracted data helps the Educational Institutions or Universities to enhance the teaching methodologies of faculties and student’s learning process in efficient manner. For improving the student performance and better educational results, the student data evaluations based on their learning modes are significant. With that note, the proposed model develops a new model called ensemble based two-level student classification model (ESCM) for effectively analysing and classifying the student data. With the student data pursuing technical higher education, the ESCM is performed with three traditional classification model and ensemble classifier techniques for enhancing the classification accuracy. The model utilizes support vector machine, Naive Bayesian and J48 classifier that are combined with Ensemble classification methods as modified meta classifier such as bagging and Stacking. Here, the technical higher education student data collected from srm student database based on the feature set contains the student learning factors that support performance enhancement. The results are evaluated with the srm student datasets and compared based on the classification accuracy and model reliability. Furthermore, the obtained results outperform the existing models. Based on the accurate predictions, special attentions and measures are taken to improve the student results and institutional reputation.

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Role of long-range coupling on the properties of single polarons in models with dual electron-phonon couplings

arXiv

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arXiv 2022年

作者： Chakraborty, Monodeep Narayan, Sankeerth S. Vigneshwaran, R. Berciu, Mona Centre for Quantum Science and Technology Chennai Institute of Technology Chennai600037 India Computer Science Engineering Department Chennai Institute of Technology Chennai600037 India Mechatronics Engineering Department Chennai Institute of Technology Chennai600037 India Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z1 Canada Quantum Matter Institute University of British Columbia VancouverBCV6T 1Z4 Canada

We use the Variational Exact Diagonalization to investigate the single polaron properties for four different dual models, combining a short-range off-diagonal (Peierls) plus a longer-range diagonal (Holstein or breathing-mode) coupling. This allows us to investigate the sensitivity of various polaron properties both to the range of the diagonal coupling, and to the specific diagonal coupling chosen. We find strong sensitivity to the range for all duals models as the adiabatic limit is approached, however considerable sensitivity is observed for some quantities even for large phonon frequencies. Also, strong dependence of the results on the specific form of the diagonal coupling is observed everywhere in the parameter space. Taken together, these results suggest that a careful consideration must be given to the specific coupling and its proper range, when quantitative comparisons with experiments are *** Codes 81Vxx Copyright © 2022, The Authors. All rights reserved.

关键词： Polarons

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Smart livestock monitoring system using IoT

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AIP Conference Proceedings 2024年第1期2853卷

作者： R. Arthi R. Sruthi S. Akash V. R. Syamsai sunder K. Divyakanth Manne Department of Electronics and Computer Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India

The country’s socioeconomic growth places the livestock sector in a special position. It also contributes significantly to the rural economy by boosting family incomes and creating gainful employment for rural residents, notably for women, landless workers, small and marginal farmers, and farmers in rural areas. 3035 million rural residents engage in livestock raising, with an average household holding of 2-3 cattle, buffalo, and 5-6 sheep or goats, which contributes to 30–40% of their income from livestock but handling livestock is a dangerous activity. Many livestock handlers cannot watch them all day long, by that time animal related accidents are happening. To stop that from happening, livestock handlers should communicate with cattle. As a solution, this paper presents a design that may aid in solving these problems.

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Artificial intelligence based identification of total knee arthroplasty implants 3

Artificial intelligence based identification of total knee a...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Ghose, Smaranjit Datta, Suhrid Batta, Vineet Malathy, C. Gayathri, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Luton and Dunstable Hospital Department of Trauma and Orthopaedic Surgery Luton United Kingdom

ISBN: (纸本)9781728170893

The identification of the make and model of the primary knee implant is an essential step for planning a revision surgery. Currently, the surgeons email the radiographs of the implant to the medical representatives of the manufacturing companies to get this information. This manual process is prone to errors and involves a considerable amount of resources and surgeon time that could be otherwise spent on more useful tasks. This study proposes an artificial intelligence based solution for the automatic identification of 6 makes of orthopedic knee implants. Deep Convolutional Neural Networks were trained on a dataset comprising 878 images of radiographs of orthopedic knee implants including both anterior posterior and lateral views. The results of the experiments showed a validation accuracy of 96.66%. Furthermore, class activation maps generated on the images when passed through the deep learning algorithm provided visual conformance to the region of interests that a surgeon would consider for identification of the make of the implant. The outcomes of this study demonstrates the effectiveness of the usage of Deep Convolutional Neural Networks for assisting orthopedic surgeons in pre-operative planning of revision surgery by accurate automated identification of make and model of orthopedic knee implant. © 2020 IEEE.

关键词： Orthopedics

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Designing a dynamic framework for people counting using YOLO-PC

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AIP Conference Proceedings 2024年第1期2919卷

作者： Kavitha Duraipandian Balaji Radhakrishnan Padmanabhan Vishesh Ranka Department of CSE SRM Institute Of Science & Technology Chennai India Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning Chennai India

Determining the count of individuals in a public space is helpful for video surveillance and security applications. The existing system which uses the Wi-Fi CSI method to count the number of individuals in a particular space by computing the disturbance caused between the range of field of the Wi-Fi, has a number of flaws, such as the inability to process recorded footage in real time or the likelihood of inaccuracies owing to the counting of persons who aren’t there. Therefore, we propose that people counting shall be done with a novel actual people counting method that already exists named YOLO-PC, the suggested system overcomes the aforesaid difficulties.

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ACoCo: An Adaptive Congestion Control Approach for Enhancing CoAP Performance in IoT Network

ACoCo: An Adaptive Congestion Control Approach for Enhancing...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： J. Jayaudhaya S. Supriya Vijay Anand Kandaswamy Samuthira Pandi V S. Kamatchi C Pushpa Priya Department of Electronics and Communication Engineering R. M. D. Engineering College Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu India Department of Electronics and Instrumentation Engineering R.M.K Engineering College Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Electronics and Communication Engineering Dhanalakshmi College of Engineering Tamil Nadu India

The Industrial Internet of Things (IIoT) requires the real-time transmission of critical data to ensure functionality and prevent hazardous situations. However, current data transmission scheduling methods in 6TiSCH networks do not efficiently handle heterogeneous traffic based on its criticality and performance requirements, potentially leading to violations of timing limits. To address this issue, this paper proposes ACoCo, an Adaptive Congestion Control approach for CoAP that uses reinforcement learning techniques to dynamically adapt congestion control parameters based on real-time network conditions, node behaviors, and traffic patterns. Simulation results demonstrate ACoCo's effectiveness in reducing end-to-end transaction delay and improving transaction delivery ratio under congested network conditions, providing valuable insights for IoT network optimization and design. ACoCo operates effectively within the 6TiSCH network architecture, taking into account the scheduling function and communication requirements of the network.

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