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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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Detecting Anomalies for corn crop disease using Isolation forest 1

Detecting Anomalies for corn crop disease using Isolation fo...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sowmiya, K. Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350336009

Today, the most challenging issue for researchers is to continue their research for crop disease using deep learning and image processing. One of the crucial detection methods used in one-class classification to find the outliers in the dataset is anomaly detection. The isolation forest approach is used in this work to discover anomalies using the corn dataset from Kaggleas a benchmark dataset. To find abnormalities in the provided dataset, this algorithm mimics a binary decision tree technique. This algorithm's classification accuracy is 89 percent. For the corn dataset, the experimental findings of the isolation forest show improved outcomes. Other anomaly identification methods will eventually combine anomaly scores and new datasets on leaf disease. © 2023 IEEE.

关键词： Image processing

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An Efficient AI Model for Classifier Tweets During Disaster Using Recurrent Neural Networks (RNN) 2nd

An Efficient AI Model for Classifier Tweets During Disaster ...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Lakshmi Narayanan, K. Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

ISBN: (纸本)9789819754113

During any significant event, particularly during natural catastrophes, social media is an essential information source. It is claimed that data generated by social networking sites would enable regular people to become more situationally aware during disasters and coordinate to help themselves because it is ubiquitous, quick, and accessible. However, as the amount of social media data has grown exponentially, so has the amount of data that are unrelated to a crisis, making it harder for individuals to find the information they need to organise relief efforts, seek assistance, and perhaps even save lives. In this essay, we offer a method for identifying. During times of tragedy, informative messages are posted on social media. Our strategy is to implement this using recurrent neural networks and demonstrate substantial progress. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Disaster Assistance Intelligent Agent 14

Disaster Assistance Intelligent Agent

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Priyadarshi, Siddhant Rastogi, Mudit Gayathri, V.M. SRM Institute of Science and Technology Computer Science Engineering Chennai India SRM Institute of Science and Technology Department of Networking with Comunication Chennai India

ISBN: (纸本)9798350335095

Virtual characters are controlled by artificial intelligence in video games and movies. Controlling these characters when they are surrounded by people is known as crowd simulation. In this work, we propose to employ intelligent agents and decision trees to simulate a realistic scenario in which our agent will lead the human behavioural AI and identify the best route to the goal or the best circumstances to the objective avoiding all the obstacles in the path. This crowd simulation will be used to make intelligent agents which can find the best path during the situation of any disaster and show humans the safest path which the humans can follow to get to the nearest safe points. Multiagent simulation, commonly referred to as a Crowd and Agent Simulation, is an approach used to simulate many people, animals or other characters interacting with each other in one place. These actors are expected to pursue their objectives, engage in the environment and coordinate with each other. It is expected that the simulated actors will pursue their goals, interact with the environment, and communicate among themselves. Crowd simulations find utility in various areas, including the enhancement of training environments and virtual worlds, the optimization of architectural planning, and the refinement of AI-driven characters' driving behaviors in video games and movies. © 2023 IEEE.

关键词： Decision trees

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Deep Learning Approaches for Disease Detection Based on Plant Leaf Image: A Review 23th

Deep Learning Approaches for Disease Detection Based on Plan...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Saraswathi, E. Banu, J. Faritha Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Bharathi Salai Tamilnadu Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Bharathi Salai Tamilnadu Chennai India

ISBN: (纸本)9783031648359

The demand for rapid and precise plant disease diagnostics has increased with interest in environmentally friendly farming techniques. Deep learning algorithms are employed in image analysis has shown significant promise for the quick and precise detection of plant diseases. Early detection of diseases in plant is crucial. Yet conventional approaches have their limits, as is discussed at the article’s outset. This review article of the relevant literature sums up the state of the art in identifying plant disease and different deep learning methods, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, which have been applied to the problem of plant disease identification. Several datasets that have been used to train and evaluate deep learning models for identifying plant diseases are also discussed in the paper. Finally, this study wraps up with a discussion of the problems and future possibilities in this subject, including the need to create more reliable and interpretable models depend on deep learning and the integration of several detection paradigms for more precise disease identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image segmentation

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Survey on Weeding Tools, Equipment, AI-IoT Robots with Recent Advancements

Survey on Weeding Tools, Equipment, AI-IoT Robots with Recen...

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2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023

作者： Michael, J Justina Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350318784

Agriculture is the backbone of many countries across the globe. Nevertheless, the challenges faced by farmers with an aim to provide quality food are enormous. From among these difficulties, weeding is the most troublesome. Right from the neolithic era till this modern era, there exist numerous solutions for eradicating weeds. Modern technologies support agriculturalists to handle these weeds in overcoming the issues. However, there still exists a gap in satisfying the real needs of a farmer. This paper presents a survey of weeds and their problems, categories of weeds, consequences of herbicide, the importance of weed removal, weeding tools, and equipment, modern AI-weeding robots, algorithms used for weed identification, and mobile applications used for weed detection. This paper also focuses on state-of-the-art techniques for weed identification and removal around the world. Future works in the area of weeds were also discussed before concluding. © 2023 IEEE.

关键词： Agricultural robots

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Optimizing Logistics with Regularization Techniques: A Comparative Study

Optimizing Logistics with Regularization Techniques: A Compa...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Grace, Anne Lourdu Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350310061

Optimising logistics operations is critical for modern businesses as it can significantly impact supply chain efficiency, customer satisfaction, and overall profitability. One way to enhance logistics performance is by employing machine learning models, such as logistic regression, to predict and optimise various logistics outcomes. However, these models often face the issue of overfitting, which can result in poor generalisation and inaccurate predictions. This paper presents a comparative study of various regularisation techniques, such as Ridge and Lasso, applied to logistic regression models for optimising logistics operations. Our results indicate that regularisation techniques can significantly improve the performance of logistic regression models in predicting and optimising logistics outcomes. The Lasso regularisation technique is particularly effective in feature selection, reducing the number of variables needed to achieve high accuracy. Our analysis furthers to the research on machine learning applications in logistics and provides practical insights for logistics managers to optimise their operations. © 2023 IEEE.

关键词： Forecasting

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Adversarial Attacks Using Carlini Wagner

Adversarial Attacks Using Carlini Wagner

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Baddukonda, Tarun Aditya Kanakam, Ronit Kumar Yasotha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the AI systems. This exposure has seen classification errors compromises and the undermining of the model performance. Understand the critical mechanisms of adversarial attacks is critical to developing strong defense mechanisms that would protect AI systems from the malevolent interventions. This abstract provides a summary of adversarial attacks as a prevalent threat to the dependability and resilience of AI technologies thus the need for continuous research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Monitoring and accelerating plant growth using IoT and Hydroponics

Monitoring and accelerating plant growth using IoT and Hydro...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kumar, Abhishek Savaridassan, P. SRM Institute of Science and Technology Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350348217

Growing plants without soil is known as hydroponics, and it is particularly helpful in places with little space and bad weather. A novel method of growing plants without soil in an inert medium is called hydroponics. By avoiding pesticides and other substances that degrade soil and land quality, hydroponics enables farmers to conserve water while enhancing crop quality. Due to its high efficiency and role as an environmentally benign substitute for soil culture, it has the ability to close the gap left by India's low agricultural productivity. This farming process has leveraged several new technologies like Internet of Things (IoT), making integration of intelligent management and real-time data monitoring and collection systems possible. This farming process also allows for a greater degree of automation and refinement in the control process of the hydroponic systems. Via this paper, we have discussed the development of a hydroponic subsystem that leverages agents for cloud-based monitoring, data collection, and storage for future analysis and heuristics. This paper presents an intuitive and low-cost approach to monitoring and accelerating plant growth based on Hydroponics and IoT with tools such as automated environment parameter monitoring and error rectification, sub-second streaming of telemetry from ground units to cloud servers and allowing users to access the status of the plant or system of plants being monitored as a plant health index from anywhere in the world, via a mobile application. The end goal of this paper is to understand the benefits one obtains by adopting a Hydroponics based cultivation system, which would show up as lower water consumption, minimal space usage, increased control over nutrients being introduced, and much more effective protection against pests and diseases when coupled with the feature of emphasizing each plant genus being cultivated via plant profile templates. This approach will not only revolutionize the millennial approach

关键词： Hydroponics

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