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Ensemble Model for Crop Prediction to Handle Extreme Dry and Wet Climate 3

Ensemble Model for Crop Prediction to Handle Extreme Dry and...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Valliyammai, C. Chitra, R. Manoj, S. Janani, R. Anna Univ Dept Comp Technol MIT Campus Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The increasing levels of greenhouse gases and climate change, including phenomena such as El Nino, have caused extreme climatic events such as floods and droughts in various parts of the Indian subcontinent, disrupting the cultivable environment for the crops. This unpredictability poses a challenge for the farmers. The proposed alternate crop recommendation system considers temperature, rainfall, soil, and market data to recommend alternate crops that are both sustainable for the climate and fetch higher prices. The proposed system uses stacking regressor with base learners such as Support Vector Regressor (SVR), Linear Regression (LR), and Random Forest Regressor (RFR) for yield prediction. The Gaussian naive Bayesian algorithm is used to calculate the standard precipitation index, which helps in classifying the drought levels. The experimental results show that the proposed system yields 97% accuracy in predicting yields with minimal root mean square error.

关键词： Climate Change Extreme Climate Alternate Crop Recommendation Ensemble learning

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FMICS: A Robust learning Methodology to Predict Skin Cancer Disease Using Fused Melanoma Image Classification Scheme

FMICS: A Robust Learning Methodology to Predict Skin Cancer ...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bhavani, G. Rama, M. Padma, S. Kumar, P. Praveen Kumar, K. Suresh Rajeshwari, R. R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Qis College of Engineering and Technology Department of It AndhraPradesh Ongole India Sona College of Technology Department of Eee Salem636005 India Qis College of Engineering and Technology Department of Ece AndhraPradesh Ongole India Panimalar Engineering College Department of Mba Chennai India S.A Engineering College Department of Master of Computer Application Chennai India

ISBN: (纸本)9798331543617

Skin cancer (SC), particularly melanoma, is a significant health issue due to its high mortality rate when not detected early. This study introduces a novel approach, FMICS: A Robust learning Methodology (Hybrid ANN + XGBoost) for predicting SC through fused melanoma image classification. The methodology integrates Artificial Neural Networks (ANN) for feature extraction and XGBoost for classification, leveraging the strengths of both models. Preprocessing techniques such as image filtering, contrast enhancement, and hair removal were applied to improve image quality. The study utilized dermoscopic images from the PH2 and HAM10000 datasets, containing both benign and malignant lesions. The hybrid model achieved superior performance compared to individual models, with an accuracy of 94.80%, sensitivity of 94.90%, and specificity of 94.70%. Data augmentation techniques, including rotation, scaling, and flipping, were applied to enhance the model's robustness. Evaluation metrics such as the F1-score and ROC-AUC were used to assess the performance. The hybrid approach significantly reduced false negatives, making it highly reliable for melanoma detection. These results suggest that the FMICS methodology offers a promising solution for accurate and efficient SC diagnosis, with potential applications in clinical and mobile environments. © 2024 IEEE.

关键词： Contrastive learning

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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Empirical Evaluation of AI Based Oral Cancer Identification Scheme Using learning Assisted Medical Image Processing Scheme

Empirical Evaluation of AI Based Oral Cancer Identification ...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vaijayanthi, S. Charulatha, G. Murugan, J. Senthil Lakshmi, N. Thenmozhi, E. Khan, S. Mahaboob Rajalakshmi Institute of Technology Department of Ai&ds Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai India Annamacharya University Department of Mechanical Engineering AndhraPradesh Rajampet India

ISBN: (纸本)9798331543617

Early detection is essential for enhancing patient outcomes, as oral cancer continues to be a substantial health challenge. This manuscript introduces the Optimized Neural learning and Classification Principle (ONLCP), a novel AI-based oral cancer identification scheme that incorporates medical image processing techniques and deep learning. Our method utilizes smart to analyze and classify medical images, thereby enabling the early detection of oral cancer. The efficiency of the proposed scheme is assessed by cross-validating it with the Convolutional Neural Network (CNN). The model is refined by this AI-based logic, which adapts to a variety of image characteristics and improves detection precision, while the ONLCP is employed for feature extraction and initial classification of oral lesions. The experimental results indicate that the AI-based scheme ONLCP obtains a prediction accuracy of 97.86%, with substantial enhancements in sensitivity and specificity when contrasted with conventional methods. The model's continuous development and adaptive learning are enabled by the integration of learning-assisted techniques, resulting in more precise and dependable cancer identification. Our results emphasize the potential of integrating AI with advanced image processing techniques to resolve critical challenges in medical diagnostics. This research has the potential to improve patient care and treatment outcomes by contributing to the growth of more efficient and scalable techniques to early oral cancer identification. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel System based on Amalgamation of Embedded Systems and Edge AI for Hand Gesture Recognition 2024

Novel System based on Amalgamation of Embedded Systems and E...

引用

16th international conference on Contemporary computing, IC3 2024

作者： Singh, Gargi Gautam, Saanika Puri, Yugnanda Parikh, Jolly Mishra, Gargi Department of Electronics and Communication Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering New Delhi India

ISBN: (纸本)9798400709722

Hand gesture recognition is highly significant and a natural means of human-computer interaction. This detection is carried out by using the MPU- 6050 sensor, a widely available Inertial Measurement Unit (IMU). MPU-6050 sensor measures the orientation and objects acceleration and is a low-cost, high-performance sensor. NanoEdgeAI studio, a low-power edge computing framework known to process data efficiently, has been employed to implement the accurate machine learning model. In addition to that, the usage of a microprocessor, Nucleo-F401RE, demonstrates the feasibility of deploying robust and responsive hand gesture recognition systems in edge computing scenarios, showcasing the potential for enhanced human-machine interaction. The selected model used for the research is MLP (multilayer perceptron) which is also known for its flexibility and ability. A balanced accuracy a percentage measure of signals correctly identified by the model was found out to be of 84.27%. © 2024 Copyright held by the owner/author(s).

关键词： Embedded systems

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Enhancing Literary Exploration: A Machine learning Approach to Book Recommendations 7

Enhancing Literary Exploration: A Machine Learning Approach ...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Singh, Kritika Gupta, Sumita Dhir, Saru Gambhir, Sapna Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Uttar Pradesh Noida India Amity University Amity School of Engineering & Technology Department of Information Technology Uttar Pradesh Noida India George Mason University College of Engineering and Computing Department of Computer Science United States

ISBN: (纸本)9798350350067

In today's era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age. © 2024 IEEE.

关键词： Collaborative filtering

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Hybrid Deep learning Architectures for Histological Image Segmentation 6

Hybrid Deep Learning Architectures for Histological Image Se...

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6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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Integrating STEM using Design Thinking Wicked Problems: A Case Study from India 14

Integrating STEM using Design Thinking Wicked Problems: A Ca...

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14th IEEE Integrated STEM Education conference, ISEC 2024

作者： Mishra, Richa Nirma Univeristy India

ISBN: (纸本)9798350352801

This research work is a work-in-progress study. Design Thinking is a way of thinking which is used in engineering classrooms to bring human-centric solutions. The study has integrated STEM by amalgamating interdisciplinary subjects in STEM using the Design Thinking approach using a collaborative teaching method. The study aimed to address the research questions: Is it possible to use Design Thinking as an approach to integrate STEM? Is it possible to raise the empathy level of the students without compromising STEM learning? Can collaborative teaching give impetus of this integration? Empathy is a part of the Design Thinking process and a very valuable attribute for engineers. The study also investigated whether students become more empathetic due to the nature of this approach. The author has used wicked problems to reach the objective. The pilot study was done by recruiting first-year students of an engineering college. The intervention has used collaborative interdisciplinary STEM teaching. The results were assessed by 1. Working prototype and 2. Statistical analysis of the pre and post-test of the engineering knowledge, and 3. Analysis of IRI index test to check empathy. The study has yielded satisfactory results. © 2024 IEEE.

关键词： Collaborative learning

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Cascade width learning based fatigue detection method

Cascade width learning based fatigue detection method

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2024 international conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Wang, Dong Wu, Pianhui Big Data and Computing Institute Guangdong Baiyun University Guangdong Guangzhou510450 China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510450 China

ISBN: (纸本)9781510685437

The fatigue detection method based on neural network has been widely applied in the field of land transportation. The accuracy varies by different methods and solutions. Disadvantages of current widely applied methods for fatigue detection have been exposed by rational review, e.g., cost-effective, potable implementation issues. Cascade width learning based detection method with outstanding features is a sound solution to overcome shortcomings of other traditional detection methods. Therefore, it is of great significance and value to research this method and present its academic value and applicable values. This paper introduces a cascade width learning based fatigue detection method. The paper looks forward to development of cascade width learning based fatigue detection method, both academic and applicable values of proposed method have been presented by rational evaluation based on rational discussion on experimental results, the results prove that proposed method has achieved 94.9% accuracy in 52.43ms, which suggests more accuracy and fast speed than other common methods including: combinations of LBP and SVM, ASM and Fuzzy, CNN and PERCLO. © 2024 SPIE.

关键词： Eye Material fatigue Machine learning Mouth Head Neural networks Education and training Signal detection Video Visualization

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Gamification of Educational Content by Using Virtual Teacher in Online learning Environment 26th

Gamification of Educational Content by Using Virtual Teacher...

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26th international conference on Interactive Collaborative learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Bondarenko, Tetiana Kovalenko, Denys Khotchenko, Iryna Zelenin, Hennadii Bozhko, Viktor Ukrainian Engn Pedag Acad Kharkiv Ukraine Kharkov Natl Univ Kharkiv Ukraine

ISBN: (纸本)9783031530210;9783031530227

A new impetus for the development of information technology in the educational process was given by the COVID-19 coronavirus pandemic and the quarantine imposed to prevent the spread of the disease. Due to the quarantine and the war in Ukraine, the educational process has moved to the online space, which has created conditions for remote learning using media content. Media education is designed to prepare students for life in the new information environment, to teach them to fully perceive different types of information, to understand and master the means of communication based on interactive forms of communication. It also improves the quality of information perception, students' engagement in interactive classes and the engagement of all participants in the learning process in the online environment. Thus, today's realities require new approaches and teaching tools that should be implemented by modern higher education institutions in the era of Education 4.0.

关键词： Digital Technologies Gamification Virtual Teacher Interactivity

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