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共 35801 条记录，以下是161-170 订阅

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Advanced Multi-Task Reinforcement Learning Utilising Task-Adaptive Episodic Memory with Hypernetwork Integration 2

Advanced Multi-Task Reinforcement Learning Utilising Task-Ad...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prasanna Kumar, R. Prasanna Kumar, B.G. Sri Ramakrishna Prabhu, A. Upadhyay, Yash Teja, Nunna Vishnu Swamy, Penta Aravinda Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

In deep reinforcement learning, sampling ineffi-ciency is addressed by mimicking human learning which lever-Ages past experiences stored in the hippocampus. Integrating this idea, the proposed approach utilizes a task-conditioned hypernetwork retrieval network with MobileNet for feature extraction from past experiences. Furthermore, BERT was in-Tegrated for advanced natural language understanding, enabling our DRL agents to better interpret and act upon language-based tasks. This approach ensures dynamic adaptability and fosters a more efficient collaboration between retrieval and decision-making processes. Evaluation in MiniGrid demonstrates that the proposed method outperforms strong baselines, showing significant advancements in deep reinforcement learning with natural language processing capabilities. This advancement not only marks progress in sampling efficiency but also opens new avenues for multi-Task learning, paving the way for more intelligent, autonomous AI systems. © 2024 IEEE.

关键词： Bidirectional Encoder Representations from Transformers Deep Reinforcement Learning Episodic Memory Hypernetwork Multi-Task Learning

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Beef Quality Assessment using InceptionNet 7

Beef Quality Assessment using InceptionNet

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bipin Nair, B.J. Sreeganesh, P.S. Karthik, N.R. Amrita Viswa Vidyapeetham School of Computing Department of Computer Science Mysuru India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Red Meat is a major supplier of essential proteins and nutrients. It is widely consumed by people around the globe and has been consumed by humans for generations. Subjective quality analysis of red meat is a difficult process. Encountering deep-learning models for the quality evaluation purpose can help quality assessment in a big way. In this research, InceptionNet has been used to classify beef images into two classes - Fresh and Rotten. A dataset of 1000 images has been used and the quality evaluation on this dataset has been carried out using three different validation split ratios. It was found that out of all test split ratios, 80:20 split has achieved the highest accuracy of 96.00% with an epoch number of 20. Future research could explore optimization, larger datasets, and practical applications to improve the prediction of beef quality across many categories. Advanced neural network architectures show promise in the assessment of meat quality. © 2024 IEEE.

关键词： Beef

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Performance Evaluation and Comparative Ranking of LLM Variants in Entity Relationship Prediction 15

Performance Evaluation and Comparative Ranking of LLM Varian...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vardhni, Krisha Devaraja, G. Dharhshita, R. Chowdary, Ritesh Krishna Mahadevan, Anbazhagan Amrita School of Computing Department of Computer Science and Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

This paper presents a comprehensive evaluation and ranking of five Large Language Models - Mistral, Vicuna, Phi, Llama 2, and Zephyr - across five temperature variants. The focus is on their ability to predict the likelihood of a relationship between pairs of entities within a given context. Each LLM variant is assessed based on stability over multiple epochs, speed, dispersion of results, and the number of incorrect responses. The study aims to provide insights into the performance of these LLMs, thereby facilitating their effective utilization in generating meaningful tabular data. The findings are expected to contribute significantly to the field of artificial intelligence and data analysis. The paper concludes with a comparative ranking of the 25 LLM variants, providing a clear perspective on their relative performances. © 2024 IEEE.

关键词： Artificial Intelligence Data Analysis Entity Relationship Prediction Large Language Models Performance Evaluation Ranking Variants Tabular Data Generation Temperature Variants

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Exploring Machine Learning Algorithms for Job Recommendation: A Focus on Career Development 15

Exploring Machine Learning Algorithms for Job Recommendation...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pallavi, M.S. Sumukha, M. Sumukh, M.R. Hegde, Vinayak Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Mysuru India

ISBN: (纸本)9798350370249

The education system plays a pivotal role in shaping individuals' careers. In addressing the challenge of recommending suitable jobs for students who are nearing the graduation, our research focuses on developing a job recommendation system leveraging machine learning (ML) algorithms. This study assesses the effectiveness of ML algorithms in categorizing jobs into sectors such as 'IT Sector', 'Teaching Sector', 'Bank Sector' and 'Business Sector'. Various parameters, including SSLC marks, PUC marks, communication skills, aptitude, and proficiency in subjects like networks, DBMS, data structures, operating systems, and programming skills, are considered. By conducting correlation analysis and employing dimensionality reduction techniques, we identify the most relevant attributes for job prediction. Three ML algorithms - Cat (categorical) Boost, Ada Boost (adaptive Boost), and XG (extra gradient) Boost - are compared for their predictive performance. A 70-30 training-testing model was employed to evaluate the performance of each model. Evaluation metrics such as accuracy, F1-score, precision, and recall were used to determine the best-performing model. Cross-validation techniques are applied to detect underfitting or overfitting. Among the three ML algorithms examined, Cat Boost emerges as the most effective, achieving a 94.7% accuracy rate in predicting job sectors. Consequently, Cat Boost is selected to build the predictive model. © 2024 IEEE.

关键词： Ada Boost Cat Boost Data Mining Student Job Recommendation XG-Boost

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A Comparison of Deep Learning Based Methods for Grading Papaya Fruit by Maturity Level 3

A Comparison of Deep Learning Based Methods for Grading Papa...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bipin Nair, B.J. Rushab, D. Kruthik, S. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798331518592

Papaya is a tropical fruit with high nutritional value. Identifying the maturity level of papaya is important in fruit industries. However, doing this manually by humans is inefficient. To automate this process, we tested two deep-learning models for classifying papaya images into four categories: ripe, partially ripe, unripe, and decayed. Previous works are focused on the classification of papaya fruit maturity into unmatured (unripe), partially mature (partially ripe), and mature(ripe), we have extended this to one more class i.e. Decayed. This class inclusion plays a vital role in distinguishing the decayed fruits from healthy ones in real-world applications and also helps in preventing the distribution of compromised produce, thereby maintaining quality control in the food supply. The primary goal is to find the best-performing deep learning model for accurate classification, which is essential for applications in agriculture and the food industry for quality control and automated sorting. We tested well-known Deep learning models: DenseNet121 and InceptionV3. Both the trained models are evaluated on the dataset comprising 1809 images. Each model's performance is evaluated using a different validation set and its classification accuracy. Our experimental results shows that the InceptionV3 model performs with the highest accuracy at 99.37% at 90:10 train-test ratio with batch size 16 under 40 Epochs, demonstrating superior performance in distinguishing between the four categories of papaya images. © 2024 IEEE.

关键词： Deep reinforcement learning

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Tire Imprint Identification and Classification using VGG19 1

引用

5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Girish, Disha N. Priyanka, M. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysore India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

In the fields of forensic research, the automobile industry, and consumer safety, classifying tire imprints is a crucial task. It involves identifying the brand or producer of a tire based on the imprints left on various surfaces. Tire impressions can be valuable evidence in criminal investigations, counterfeit identification, product quality control, and accident reconstruction. Here, we propose a deep learning approach using the VGG19 architecture for tire impression identification and classification. To begin, we collected tire impressions from various two- and four-wheeler MRF tire brands. Our dataset consists of 2693 photos divided into 22 classes, with 15 classes representing 2-wheelers and 7 classes representing 4-wheelers. We processed the images through denoising, normalization, and enhancement steps. We then employed a mask-based segmentation method to extract texture-based information from the tire impressions. This, combined with morphological data, resulted in a comprehensive set of features used in pattern recognition. These features include statistical measurements such as mean, entropy, correlation, contrast, and others. To accurately determine the tire brand, we utilized the Structural Similarity Index Matrix (SSIM) with a minimum similarity index value of 0.7. By comparing the SSIM values, we were able to match the tire impressions to the correct brand. We divided the dataset into 80% training data and 20% test data, which provided the best performance for our proposed technique. The accuracy achieved using this approach was 95.3%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automotive industry

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Divide and Conquer: Legal Concept-guided Criminal Court View Generation

Divide and Conquer: Legal Concept-guided Criminal Court View...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xu, Qi Wei, Xiao Yu, Hang Liu, Qian Fei, Hao School of Computer Engineering and Science Shanghai University China School of Computer Science University of Auckland New Zealand School of Computing National University of Singapore Singapore

ISBN: (纸本)9798891761681

The Criminal Court View Generation task aims to produce explanations that inform judicial decisions. This necessitates a nuanced understanding of diverse legal concepts, such as Recidivism, Confess, and Robbery, which often coexist within cases, complicating holistic analysis. However, existing methods mainly rely on the generation capability of language models, without paying enough attention to the important legal concepts. To enhance the precision and depth of such explanations, we introduce Legal Concept-guided Criminal Court Views Generation (LeGen), a three-stage approach designed for iterative reasoning tailored to individual legal concepts. Specifically, in the first stage, we design a decomposer to divide the court views into focused sub-views, each anchored around a distinct legal concept. Next, a concept reasoning module generates targeted rationales by intertwining the deconstructed facts with their corresponding legal frameworks, ensuring contextually relevant interpretations. Finally, a verifier and a generator are employed to align the rationale with the case fact and obtain synthesized comprehensive and legally sound final court views, respectively. We evaluate LeGen by conducting extensive experiments on a real-world dataset and experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at https://***/xuqi220/LeGen. © 2024 Association for Computational Linguistics.

关键词： Crime

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Deep Adaptive Algorithms for Local Urban Traffic Control: Deep Reinforcement Learning with DQN 2

Deep Adaptive Algorithms for Local Urban Traffic Control: De...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Subba Rao, P.V.R. Polisetty, Venkata Ramana Murthy Jayanth, K Krishna Sai Manoj, N.V. Mohith, Vankadari Kumar, R. Prasanna Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

The paper uses Deep Reinforcement Learning (DRL) to traffic signal regulation, solving urban traffic congestion. Our research paper demonstrates the simulation of intricate traffic conditions with microscopic accuracy, utilizing the Simulation of Urban Mobility (SUMO) platform. As the foundation for DRL, a state representation and action space are established. The learning process is guided by the Deep Q-Network (DQN) architecture, precisely set hyperparameters, and epsilon-greedy exploration. To improve stability, training data is gathered and stored in an experience replay buffer. The DQN-based technology is built into the SUMO simulation and allows for dynamic traffic signal modifications. Updates to the target network on a regular basis increase stability. Metrics for evaluation include average waiting time, traffic flow efficiency, and congestion levels, which compare favorably to baseline approaches. In conclusion, this study shows the usefulness of DRL in optimizing traffic signal control, providing viable strategies to minimize urban traffic congestion and increase transportation efficiency. © 2024 IEEE.

关键词： Congestion Reduction Deep Reinforcement Learning DQN Architecture Epsilon-Greedy Exploration SUMO Traffic Signal Control

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Breast Cancer Detection by Improving Feature Selection and Classify using Ensemble Artificial Neural Network method 3

Breast Cancer Detection by Improving Feature Selection and C...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sridevi, G. Sharmila, K. School of Computing Sciences Department of Computer Science Chennai Pallavaram600117 India

ISBN: (纸本)9798350369083

he most frequent cancer endured to the women is Breast Cancer (BC) and one in every eight women get affected by BC from their age of forty. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. The sample have considered from WDBC dataset in which essential features have been selected through the BORUTA feature selection algorithm, which prepares conditional variables corresponding to features for a prediction model based on rough set theory (RST). This paper majorly focus on high accuracy in detection of BC through propose an advanced ensemble technique, which incorporates voting classification in the keras of Artificial Neural Network (ANN) techniques for the classifier to distinguish benign breast tumors from malignant cancers. In the feature extraction process, considered the most frequently repeated process to find the most important features of the WDBC that are pertinent to BC detection and classification. The combination of boruta algorithm with ensemble based ANN model has reduces the false positive and false negative errors using ensembling techniques that combine several base models to produce one optimal predictive model. The valuation of the proposed model accuracy as 97.36% is comparatively high than traditional ANN method and boruta with ANN model in detect

关键词： Lung cancer

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Adaptive QoS Protocol in Systemized Networks with Machine Learning 2

Adaptive QoS Protocol in Systemized Networks with Machine Le...

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

作者： Nitish Rao, K.P. Amith Kumar, S. Keerthika, K. School of Computing Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (纸本)9798350372892

In today's networking world, Quality of Service (QoS) is essential for guaranteeing that data packets are delivered efficiently. Understanding metrics like jitter, throughput, and latency is also important for meeting the needs of different users and applications. As a result, QoS techniques have a direct impact on the network and may be overly inflexible in an operator-centric setting. This article describes a revolutionary approach to network QoS management based on machine learning (ML) algorithms such as KNN, random forest, and decision trees. In this research, NS3 simulations and real-world data are used to train and test the machine learning models on several QoS metrics, demonstrating their automation potential. The findings of this study pave the door for dynamic QoS adjustment, which would enhance service delivery. © 2024 IEEE.

关键词： Adversarial machine learning

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