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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Nagendra Prabhu, S. Singh, Amarjeet Bhavya, B. Sreeja, K. Bhargavi, P. Srm Institute of Science Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665400527

Recently, Stress Prediction in every individual based on their profile and behaviour is a challenging task in the current sector. Current system is a manual process where it is difficult to identify the stress in the college students or employees. There is no automation for any stress prediction. System uses machine learning algorithms or AI algorithms to find out the stress levels, these technologies are used for application development. The purpose of this project is to reduce stress in students and employees. Computer science has come a long way in the last few years. It is massive and multifaceted. It has been used in a variety of applications to meet the basic needs of human society. In the field of healthcare, machine learning has made significant progress. Stress is a deadly disease that kills a large number of people worldwide. How machine learning techniques can assist in reducing the risk of stress prediction, is examined in this study. © 2022 IEEE.

关键词： Personnel

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An Improved Cuttlefish Optimization Algorithm Based Faster R-CNN for Brain Tumour Detection 4

An Improved Cuttlefish Optimization Algorithm Based Faster R...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Arunarani, A.R. Bhenia, Vedant Abirami, T. Aparna, N. Abul Ala Walid, Md. Sharada, K.A. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Campus Department of Computational and Intelligence Tamilnadu Chennai India Ips Academy Institute of Engineering and Science Department of Computer Science and Engineering Madhya Pradesh Indore India Kongu Engineering College Information Technology Tamilnadu Erode India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering Khulna Bangladesh Hkbk College of Engineering Vtu Department of Cse Karnataka Bengaluru India

ISBN: (纸本)9798350300888

Brain tumor is caused due to the anomalous development of tissues in the brain. Precise identification of the tumor in premature stage is essential to provide effective medication and curtail further impact of the disease in affected individuals. This present research includes four main stages for the diagnosis of brain tumor including segmentation and classification based on deep learning approaches. The preprocessing stage makes use of filters to reduce the noise impact in the brain imagery. The segmentation stage is responsible to segment the tumor portions from the images using Enhanced Region Growth algorithm. Feature selection stage utilizes Improved Cuttlefish Optimization (ICO) algorithm to extricate the optimal features from the segmented images. The final stage is classification in which Faster R-CNN (FRCNN) algorithm is employed to categorize the brain images as tumor affected or not. BRA TS-2020 dataset is used for the experimental investigation of the proposed FRCNN-ICO method. Performance of this method is assessed using parameters such as Accurate Prediction Rate (APR), Imprecise Prediction Rate (IPR) and Imprecise Denial Rate (IDR). The proposed FRCNN-ICO produces the highest APR value of 99.2% compared to the existing researches on brain tumor detection and proves to perform superior than the methods considered for comparison purposes. © 2023 IEEE.

关键词： Tumors

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia Computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier 2

Oral Cancer Detection and Classification Using Deep Learning...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Anitha, D. Soujanya, Thota Chakraborty, Subhra Alkhayyat, Ahmad Revathi, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%. © 2024 IEEE.

关键词： Gabor filters

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Leveraging Deep Learning and Blockchain for Enhanced Transparency and Traceability in the Indian Herbal Product Supply Chain

Leveraging Deep Learning and Blockchain for Enhanced Transpa...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Prateek Uniyal Charanjeet Singh G. Dinesh Dept of Computational Intelligence School of Computing School of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Medicinal plants have been a cornerstone of healthcare practices globally, serving a significant population in developing countries who rely on affordable herbal remedies. However, the current marketing system through Mandis and wholesale markets suffers from opacity. Numerous intermediaries create an information gap for farmers regarding fair pricing and market trends. Additionally, concerns exist about misidentification and adulteration of medicinal plants due to limited knowledge among collectors and traders. The National Medicinal Plants Board (NMPB) recognizes the need for a robust supply chain. This paper proposes a blockchain-based platform ‘Indian Herbal Blockchain Network (IHBN)’ integrated with Deep Learning for image processing. The deep learning model has achieved a decent accuracy of 90.24% which decreases the chance of misidentification. The platform ensures transparent traceability of medicinal plants from source to consumer, along with accurate identification using image analysis. This system promotes transparency within the medicinal plant supply chain, reducing the risk of adulteration and improving overall trust.

关键词： Medicinal plants Deep learning Accuracy Supply chains Sociology Pricing Medical services

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Adaptive Agriculture System with Soil Health Prediction using Ensemble Techniques and Smart Irrigation using Deep Learning

Adaptive Agriculture System with Soil Health Prediction usin...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Vasan J K Rithvidas R Hariprasad S M Maria Dominic Savio R S Haritha Sudeshna Shettygari Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Agriculture is a cornerstone of the country’s economy, serving as a fundamental necessity for human survival and a critical driver of socio-economic stability. In traditional farming, the efficiency of crop yield is often limited, and resources are not managed effectively, leading to a significant reduction in overall productivity. This paper introduces an IoT-based adaptive agriculture system using a real-time soil health prediction algorithm, where it leverages the Custom Stacked Ensemble model combined with K-Fold Stratified cross-validation to generate predictions from dynamic environmental and soil conditions. The proposed model achieves an accuracy of 94.5%. Additionally, the system incorporates a smart irrigation mechanism that regulates water delivery to crops, ensuring optimal growth while conserving water resources. The irrigation process is automated using the Enhanced GRU model, which achieves an accuracy of 99.43%, further enhancing efficiency and sustainability. The system collects the data from the sensor in the field via a LoRa-based network that communicates with each other to transmit the data to the IoT server. By combining the weather forecasts and the data obtained from the sensors, the system gives soil suitability for the farmer and also predicts the precise irrigation levels and soil fertility, aiding adaptive farming.

关键词： Irrigation Adaptation models Adaptive systems Accuracy Weather forecasting Soil Prediction algorithms Inference algorithms Water resources Farming

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A Dynamic Pyramidal Relaxation Method of AC OPF 9

A Dynamic Pyramidal Relaxation Method of AC OPF

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Tang, Yu-Yang Chen, Liang Du, Hongbo Chen, Sheng-Jie Kou, Caixia Ai, Xiaomeng Zhou, Bo Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications College of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China University of Michigan Department of Industrial and Operations Engineering Ann Arbor United States

ISBN: (纸本)9798350309638

The Alternating Current Optimal Power Flow (AC OPF) is crucial for power system analysis, yet existing algorithms face challenges in meeting the diverse requirements of practical applications. This paper presents a Pyramidal Relaxation (PR) formulation of the Branch Flow Model (BFM) for AC OPF, where surface conical constraints are approximated through a union of tetrahedrons in a Mixed Integer Linear Programming (MILP) relaxation. To reduce problem size and to enhance computational efficiency, we proposed a Dynamic Pyramidal Relaxation (DPR) method, where the cuts (inner approximation and outer approximation) are adaptively added. In practice, the DPR method is embedded into a MILP commercial solver based on the branch-and-cut framework. Case studies in different test systems validate the effectiveness in terms of feasibility, accuracy, and efficiency of our proposed method. © 2024 IEEE.

关键词： computational efficiency

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Enhanced Fruit Fly Optimization Algorithm for Wrapper Based Feature Selection in Cancer Prediction

Enhanced Fruit Fly Optimization Algorithm for Wrapper Based ...

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Cognitive, Green and Ubiquitous computing (IC-CGU), International Conference on

作者： P. Saranya D. Viji Aditya Jangiti Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems.

关键词： Sensitivity Machine learning algorithms Lung cancer Medical services Feature extraction Prediction algorithms Breast cancer

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability 8

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Reddy, M.Jaganmohan Ramu, V. Grace Shalini, T. Vincysahaba, R. Devaki, M. Athiraja, A. Telangana Secunderabad India Telangana Hanamkonda India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Computational Intelligence Chengalpattu India St.Joseph College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Velammal College of Engineering and Technology Department of Eee Tamil Nadu Madurai India

ISBN: (纸本)9798350367904

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions. © 2024 IEEE.

关键词： Precision agriculture

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IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots 3

IoT-Enhanced Path Planning and Obstacle Avoidance for Autono...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Selvanathan, N. Ramya, S.R. Sureshkumar, C. Kiruthiga, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Sona College of Technology Department of Information Technology Salem India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of AI&DS Erode India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350354171

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can't keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the D∗ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D∗ Lite's quick replanning skills with DRL's ability to learn and adapt made even better by live data from IoT sensors. We've put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it's a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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