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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Majumder, Sayan Khandelwal, Yash Sornalakshmi, K. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9. © 2024 IEEE.

关键词： Cultivation

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Dual-path Residual UNet with Convolutional Attention based Swin-Spectral Transformer Network for Segmentation and Detection of Ovarian Cancer 3

Dual-path Residual UNet with Convolutional Attention based S...

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3rd International Conference on Advanced computing Technologies and Applications, ICACTA 2023

作者： Shanthini, A. Boyanapalli, Arathi Srm Institute of Science and Technology Kattankulathur School of Computing Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350348347

In this paper, a new transformer based deep learning network (TDLN) model has designed for detecting ovarian cancer (OC) from the input samples. Primarily, the input images are pre-processed to remove the unwanted noises and thereby enhances the quality through HSV color channel conversion, block matching with triple dimensional filtering (BM_TriD-Fil) and improved gabor wavelet transform (IGWT). From the pre-processed images, the affected regions are gets segmented through a new dual-path residual UNet (DPResU-Net) structure. Using the segmented regions, the feature extraction and classification is performed by utilizing a novel convolutional adaptive-attention based swin-spectral transformer network (CA2_S2TNet) model. In order to tune the parameters of CA2_S2TNet model, chaotic fire hawks optimization (CFHO) has employed. The proposedTDLN model is implemented in the python platform through TCGA-OV dataset and assessed the performance in terms of different evaluation measures. The maximum classification accuracy obtained by TDLN is 99.91%, superior to the existing approaches for OC classification. © 2023 IEEE.

关键词： Image enhancement

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ML-driven Emotion Identification for Feedback Analysis in E-learning Platforms

ML-driven Emotion Identification for Feedback Analysis in E-...

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2024 OPJU International Technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chitteti, Chengamma Kopparam, Runvika Ganesh, B.V.S.S. Sutraya, Sukerthi Kamakshi, V. Jangam, Shiridinath School of Computing Dept. of Data Science Tirupati India Dept. of Csse Tirupati India G. Narayanamma Institute of Technology and Science for Women Dept. of Data Science Hyderabad India

ISBN: (纸本)9798350373783

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves 90.3% accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions. © 2024 IEEE.

关键词： Contrastive Learning

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Jointly Defending DeepFake Manipulation and Adversarial Attack Using Decoy Mechanism

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第8期45卷 9922-9931页

作者： Chen, Guan-Lin Hsu, Chih-Chung National Cheng Kung University Institute of Data Science Tainan70101 Taiwan Miin Wu School of Computing Tainan70101 Taiwan

Highly realistic imaging and video synthesis have become possible and relatively simple tasks with the rapid growth of generative adversarial networks (GANs). GAN-related applications, such as DeepFake image and video manipulation and adversarial attacks, have been used to disrupt and confound the truth in images and videos over social media. DeepFake technology aims to synthesize high visual quality image content that can mislead the human vision system, while the adversarial perturbation attempts to mislead the deep neural networks to a wrong prediction. Defense strategy becomes difficult when adversarial perturbation and DeepFake are combined. This study examined a novel deceptive mechanism based on statistical hypothesis testing against DeepFake manipulation and adversarial attacks. First, a deceptive model based on two isolated sub-networks was designed to generate two-dimensional random variables with a specific distribution for detecting the DeepFake image and video. This research proposes a maximum likelihood loss for training the deceptive model with two isolated sub-networks. Afterward, a novel hypothesis was proposed for a testing scheme to detect the DeepFake video and images with a well-trained deceptive model. The comprehensive experiments demonstrated that the proposed decoy mechanism could be generalized to compressed and unseen manipulation methods for both DeepFake and attack detection. © 1979-2012 IEEE.

关键词： Generative adversarial networks

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APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR EPIDEMIC MODELING

APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR EPIDEM...

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2024 Winter Simulation Conference, WSC 2024

作者： Villaplana, Hannah Danielle Ladera Kwak, Jaeyoung Lees, Michael H. Li, Hongying Cai, Wentong College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (纸本)9798331534202

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents’ decision-making using random forest model. © 2024 IEEE.

关键词： COVID-19

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Explaining Grover's Quantum Algorithm to College Students

Explaining Grover's Quantum Algorithm to College Students

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2023 Congress in Computer science, Computer Engineering, and Applied computing, CSCE 2023

作者： Deligiannidis, Leonidas School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350327595

Wentworth institute of Technology is a baccalaureate degree granting institution and in recent years we have also developed several Master's programs. Our department offers several degrees leading to a Bachelor of science in Computer science, Computer Networking, Information Technology, Cybersecurity, and data science. All our programs are designed to be hands-on and expose students to a wide skill set. All our majors need to take several security-related courses. One of the latest security related courses we developed is in Quantum computing with a focus on security. Quantum computing is a groundbreaking technology that exploits the laws and properties of quantum mechanics. It can dramatically speed up computations of certain problems and surpass the computation power of modern supercomputers. One notable quantum algorithm we cover in our newly developed course is Grover's algorithm. It is used to search an unstructured database while providing quadratic speedup, compared to its classical counterpart. While demonstrating Grover's algorithm in the classroom, the students raise many questions and explaining all the details of the algorithm is challenging. In this paper, we explain and illustrate the theoretical and implementational challenges and provide examples and solutions. © 2023 IEEE.

关键词： Grover's algorithm Quantum computing

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Rough set model based on variable universe

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 503-511页

作者： Qingzhao Kong Xueer Chang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute XiamenChina

Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis of many data problems,it is more reasonable to select parts of objects which are useful to us or can meet the actual needs as a ***,in order to make up for the deficiency of traditional models,a new model is introduced from the perspective of variable ***,some interesting properties of this model,such as approximation sets,reduct of attributes and maximum part of universe,are *** the study of this paper,it can be seen that the model developed in our paper is not only more accurate but also more effective in describing uncertain knowledge.

关键词： approximation universe describing

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Analysis of the Influence of Green Artificial Intelligence on Agricultural and Biological Fields Using Bibliometrics 5

Analysis of the Influence of Green Artificial Intelligence o...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Anand, Jonitha Ab Ghani, Hadhrami Yusoff, Nooraini Kumar Thoti, Kiran Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia M.S. Ramaiah Institute of Management Bangalore India

ISBN: (纸本)9798331528553

The research examines how Green Artificial Intelligence (AI) impacts agricultural and biological domains by analysing relevant literature using bibliometrics. Green AI, which prioritizes sustainability and environmental awareness, is proving to be an asset in tackling the issues in agriculture and biology. The investigation examines a wide range of academic publications, patents, and research papers to identify significant trends, research topics, and the influence of Green AI on agricultural and biological fields. The research shows an increasing amount of literature investigating the combination of AI technology with green principles to improve resource management, boost agricultural output, and reduce environmental effects. This study uses analysis of citation networks, co-authorship patterns, and theme grouping to get insights into the present status and future trends of research in the fields of Green AI, agriculture, and biology. The data was extracted from the database maintained by Scopus using the PRISM Model. 11,142 article data points were used for the analysis of Green AI. This bibliometric study enhances comprehension of the changing environment of Green AI applications in agricultural and biological areas, providing vital recommendations for academics, policymakers, and practitioners working towards sustainable development objectives. © 2024 IEEE.

关键词： Biotic

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