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An Advance Boosting Approach for Multiclass Dry Bean Classification

Journal of Engineering Science and technology Review 2023年第2期16卷 107-115页

作者： Nayak, Janmenjoy Dash, Pandit Byomokesha Naik, Bighnaraj Dept. of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada757003 India Dept. of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Dept. of Computer Application Veer Surendra Sai University of Technology Burla768018 India

Dry beans, which are produced in large quantities, have the highest level of genetic diversity. The quality of seeds has a significant impact on crop yield. The importance of seed classification to both marketing and production can be shown by realizing that sustainable agricultural systems depend on these principles. This research is primarily aimed at providing a means to generate uniform seed varieties, as seed is not certified as a single variety. To achieve consistent seed classification, we have proposed Extreme Gradient Boosting ensembles using the Synthetic Minority Over-Sampling Methodology (SMOTE) to differentiate seven distinct registered types of dry beans with similar characteristics. There were a total of 13,611 grains from seven different varieties of dry beans sampled for the classification model. Classification algorithms based on machine learning like Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Adaptive Boosting classifier, Bagging Classifier, and Extreme Gradient Boosting ensembles using the Synthetic Minority Over-Sampling Methodology (SMOTE) were developed and compared. Overall correct classification rates for SVM, MLP, DT, Adaboost, Bagging, and Extreme Gradient Boost classifiers were 94.44%, 94.48%, 96.53%, 96.35%, 96.89%, and 97.32%, respectively. Extreme Gradient Boosting ensembles using the SMOTE classification model have the best accuracy. The results of this study satisfy the producers' and customers' demand for uniform bean varieties. © 2023 School of Science, IHU. All rights reserved.

关键词： Adaptive boosting

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MediAI: An AI Based Guide for Medical Content Authenticity Analysis

MediAI: An AI Based Guide for Medical Content Authenticity A...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Jadhav, Dhiraj Dighe, Aneesh Walunj, Ajinkya Sagri, Anish Bhandari, Akash Ashtagi, Rashmi Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331517953

In the Internet age, it is critical to identify the legitimacy of accurate medical content. This proposed research offers a thorough NLP-driven architecture intended to improve the veracity and correctness of online medical material. The proposed system effectively differentiates AI-generated content from non-AI content by leveraging Machine Learning (ML) models for testing and Deep Learning (DL) models for implementation, particularly Long-Short Term Memory (LSTM) and Bidirectional Long-Short Term Memory (Bi-LSTM). This will help decrease the dissemination of medical misinformation with the use of higher techniques of ML and DL, hence greatly improving the level of public health literacy. The proposed system will be user-friendly and will provide real-time verification;hence it would be very useful for healthcare professionals, the general public, in effect leading to a better-informed and health-conscience society. © 2024 IEEE.

关键词： Electronic health record

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Optimizing Location Allocation in Urban Management: A Brief Review 29

Optimizing Location Allocation in Urban Management: A Brief ...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Ayati, Aref Naji, Hamid Reza Hashemi, Mohammad Mahdi Saffar, Mohsen Dept. of Computer Engineering and Information Technology Graduate University of Advanced Technology Kerman Iran

ISBN: (纸本)9798331523114

Various issues in the fields of urban management, digital transformation, and smart cities have been presented recently. Currently, we like to attend to optimizing location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. This review examines heuristic and meta-heuristic approaches, including GA, PSO, and GIS-based models, highlighting their applications and limitations in urban management optimization. Finally, a brief conclusion will be made of the existing methods and some of their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field. © 2025 IEEE.

关键词： Urban transportation

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. The smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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Communication through Indian Sign Language: An Approach for Data Preprocessing

Communication through Indian Sign Language: An Approach for ...

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2023 International Conference on computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Khadkikar, Prajakta Shah, Himanshu Chaudhary, Shubham Kalekar, Shlok Chavda, Jenish Pune Institute of Computer Technology Dept. of Computer Engg Pune India

ISBN: (纸本)9798350338003

People who are specially impaired with regards to speaking and hearing find it difficult to communicate with people who are not impaired. The same is true vice-versa. For this very reason, Sign Language is necessary to be known and understood by both the parties. However, there are many people who are completely unaware of Sign Language and its importance. Hence, this project is aimed at bridging this communication gap between the two parties, and eventually, have an awareness on this importance of Sign Language. The paper presents an introductory analysis for the development of the Indian Sign Language (ISL) Recognition System. The primary focus of the paper is to discuss various steps involved in the formation, preprocessing, and augmentation of the dataset. The paper also briefly discusses the various steps performed after preprocessing to train and implement the model, with the focus on the systems based on Artificial Neural Networks. © 2023 IEEE.

关键词： Image classification

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IOT Based Multicrop Soil Moisture Control and Monitoring System 1

IOT Based Multicrop Soil Moisture Control and Monitoring Sys...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Barhate, Madhusudan Desai, Devam Mishra, Gaurav Phadke, Gargi Das, Siuli Bhuran, Supriya Yogesh Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Dept. of Instrumentation Engg Dept. of Computer Engg Dept. of Computer Science and Engg Navi Mumbai India

ISBN: (纸本)9798350355611

Agriculture is crucial for the economy, with China and India leading in production. Conventional irrigation methods require precise timing and quantity, varying for different crops. This paper proposes an IoT-enabled intelligent irrigation system to enhance crop productivity. The system uses a moisture sensor to activate the pump motor, and IoT technology enables remote monitoring through a web interface or mobile app. The system stores moisture data customized to specific crop requirements, reducing human intervention while ensuring optimal water supply. © 2025 IEEE.

关键词： Moisture control

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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