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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job-Shop Scheduling

IEEE Transactions on Evolutionary Computation 2025年第3期29卷 749-763页

作者： Liu, Yun Zhang, Fangfang Sun, Yanan Zhang, Mengjie Sichuan University College of Computer Science Chengdu610065 China Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Dynamic flexible job-shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration of the performance. In this article, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state feature extraction method is first developed, which can effectively represent different job-shop scenarios. Furthermore, a genetic encoding strategy is designed, which can reduce the encoding length to enhance search ability. In addition, an evaluation strategy is proposed to calculate a fixed target for each job-shop state, which can avoid the parameter update of target networks. With the designs, the DQNs could be stably trained, thus their performance is greatly improved. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art peer competitors in terms of both effectiveness and generalizability to multiple scheduling scenarios with different scales. In addition, the ablation study also reveals that the proposed algorithm can outperform the DQN algorithms with different updating frequencies of target networks. © 1997-2012 IEEE.

关键词： Job shop scheduling

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COMET ■: “cone of experience” enhanced large multimodal model for mathematical problem generation

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Science China(Information Sciences) 2024年第12期67卷 97-98页

作者： Sannyuya LIU Jintian FENG Zongkai YANG Yawei LUO Qian WAN Xiaoxuan SHEN Jianwen SUN National Engineering Research Center of Educational Big Data Central China Normal University Faculty of Artificial Intelligence in Education Central China Normal University School of Software Technology Zhejiang University

The impact of generative artificial intelligence on education is unprecedented [1]. Researchers have been exploring possibilities of combining the large multimodal model(LMM)with the teaching process. Specifically, Luo and Yang [2]have explored large model collaborative domain models to support smart education, fostering personalized and adaptive educational experiences. However, existing studies still lack in-depth research on generating educational resources,especially in mathematical problem generation.

关键词： Personnel training

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Reflecting real-time monitoring of Agricultural equipment in Rythu Bharosa Kendra 2

Reflecting real-time monitoring of Agricultural equipment in...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Vasavi, S. Mupparisetty, Bhargav Reddy Vemula, Narasimha Rao, A. Nageswara Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence & Data Science Vijayawada India Vijayawada India

ISBN: (纸本)9798350360523

The Rythu Bharosa Kendra (RBK), an initiative by the Andhra Pradesh government, seeks to bolster farming communities by supplying quality seeds, fertilizers, and agricultural supplements while offering vital extension advisories at the village level. To modernize their operations, a proposed automated system will replace manual record-keeping by storing data directly into a database, facilitating seamless facility transactions, and generating comprehensive service reports. By outfitting RBK vehicles with GPS devices and integrating React technology for a user interface, administrators gain real-time insights into vehicle movements, optimize routes, and efficiently monitor the distribution of agricultural resources. This innovative approach not only streamlines RBK's processes but also ensures timely and effective support to farmers while minimizing paperwork and enhancing overall operational efficiency. © 2024 IEEE.

关键词： Global positioning system

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VisionSaver: Revolutionizing Ocular Disease Detection with AI-Powered Real-Time Screening 1

VisionSaver: Revolutionizing Ocular Disease Detection with A...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Benedict Vinusha, V. Indhuja, V. Reddy, Medarametla Varshitha Nikhitha, Nagalla Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350372748

Detecting ocular diseases is crucial for preventing vision impairment and blindness. However, various obstacles such as insufficient awareness, elevated healthcare expenses, and the necessity for technological advancements impede effective screening. This ground-breaking device, VisionSaver, presents a novel method of identifying eye pathologies. By employing a multi-layered deep Convolutional Neural Network, VisionSaver outperforms traditional models in the extraction of complex characteristics from medical images. VisionSaver stands out for its real-time detection capabilities, which are made possible by an easy-to-use Graphical User Interface. This feature gives medical professionals accurate results and makes it possible to make decisions quickly. The system's adaptability guarantees precise identification of a broad range of ocular diseases, encompassing Diabetic Retinopathy, glaucoma, and even uncommon ocular conditions. VisionSaver enhances efficiency and retains crucial features by integrating sophisticated techniques like convolutional layers, pooling layers, dense layers, and VGG models. This innovative strategy holds the potential to transform eye healthcare, ushering in a proactive treatment era and significantly enhancing patient outcomes. VisionSaver combines data collection and preparation, data preprocessing, feature extraction, model selection, and model training to provide a robust solution for ocular disease detection achieving the accuracy of 91%. The system's real-time capabilities, user-friendly interface, and adaptability make it a powerful tool in the fight against ocular diseases. © 2024 IEEE.

关键词： Medical imaging

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Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches 8

Developing Question-Answering Models in Low-Resource Languag...

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8th International artificial intelligence and data Processing Symposium, IDAP 2024

作者： Incidelen, Mert Aydogan, Murat Firat University Department of Artificial Intelligence and Data Engineering Elazig Turkey Firat University Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798331531492

In this study, transformer-based pre-trained language models were fine-tuned using medical texts for question-answering (QA) tasks in Turkish, a low-resource language. Variations of the BERTurk pre-trained language model created using large Turkish corpus were used for QA tasks. The study presents a medical Turkish QA dataset created using Turkish Wikipedia and medical theses located in the Thesis Center of the Council of Higher Education in Turkey. This dataset, containing a total of 8200 question-answer pairs, is used to fine-tune the BERTurk model. The performance of the models was evaluated by Exact Match (EM) and F1 score. The BERTurk (cased, 32k) model achieved an EM of 51.097 and an F1 score of 74.148, while the BERTurk (cased, 128 k) model achieved an EM of 55.121 and an F1 score of 77.187. The results show that pre-trained language models can be successfully used for question-answer tasks in low-resource languages such as Turkish. This study lays an important foundation for Turkish medical text processing and automatic QA tasks and sheds light on future research in this field. © 2024 IEEE.

关键词： Medical education

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A Robust Approach to E-Banking Phishing Detection using Ensemble Methods and LSTM 1

A Robust Approach to E-Banking Phishing Detection using Ense...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Siva Reddy, Naga Venkata Rohith Saai, A.R. Ramanujan, T.V. Reddy, N Rajesh Kumar Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350372748

Recently, the integration of Internet and banking technologies has shifted financial transactions primarily to online platforms. However, this transformation has exposed the financial sector to cyber threats, particularly phishing, resulting in substantial annual losses and widespread data breaches. Despite ongoing efforts to combat phishing, a comprehensive solution remains elusive, creating a vulnerability for cyber attackers to unlawfully access sensitive personal and financial data. In this research, a novel method for successfully recognizing phishing attempts is presented. This proposed methodology includes components such as URLs, HTML, and recurring themes in phishing efforts, which are thoroughly analyzed. These characteristics are consistently similar, resulting in a significant amount of data being collected and sent over the Internet. To preserve the integrity of online financial transactions and shield people from data breaches, a robust phishing attack detection system must be developed. This study applies machine learning techniques such as LSTM, Boosting, Random Forests, and Ensemble models for intelligent phishing detection, attaining a remarkable 97 percent accuracy. To capture complicated data relationships, minimize overfitting, and improve generalization performance, a voting classifier combines predictions and stacking and uses feedback from several models. By adopting this strategy, defenses against the constantly changing cyber threat landscape in the digital financial transaction environment are strengthened. © 2024 IEEE.

关键词： Learning systems

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Natural Language Translation Engine For Announcements and Information Dissemination At Stations 2

Natural Language Translation Engine For Announcements and In...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Reedy, Medarametla Varshitha Abdul Naeem, B. Reddy, Tunga Kartikeya Benedict Vinusha, V. Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350328202

In today's fast-paced global travel landscape, effective communication is crucial, especially in dynamic environments like railway stations. This research paper addresses linguistic diversity challenges by developing a cutting-edge natural language translation engine tailored for railway communication. It integrates advanced technologies such as Transformer-based Automatic Speech Recognition (ASR) with noise reduction algorithms like Wav2Vec 2.0 and an Attention-based Convolutional Recurrent Neural Network (CRNN) to overcome background noise and diverse speech patterns. The system also includes multilingual Text-to-Speech (TTS) and Machine Translation (MT) capabilities for clear communication in real-time across multiple languages. Interactive features like a responsive Chatbot and Interactive Voice Response System (IVRS) enhance user engagement. This approach meets passengers' linguistic needs while promoting inclusivity. Technically, the project explores algorithms like the Transformer model, positional encoding, self-attention mechanisms, and feed-forward neural networks. The research aims to revolutionize railway communication and establish a precedent for technology-driven public transportation, creating universally accessible and user-friendly travel experiences. © 2024 IEEE.

关键词： Recurrent neural networks

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mewada, Dhruv Gaikwad, Shlok Gharat, Bhavika Kamble, Priyanshu Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence&Data Science Mumbai India

ISBN: (纸本)9798350359688

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness. © 2024 IEEE.

关键词： online tests proctoring remote learning

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Boothless Toll Collection System 4

Boothless Toll Collection System

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sarkar, Swagata Selvabharathi, M. Sarveswaran, D. Arun Raja, M. Raju, Aswin Sri Sairam Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

As a developing country, it is critical that we prioritize the construction and improvement of our road infrastructure in order to stimulate economic growth and promote easier logistics operations. However, the current toll collection system is hampered by difficulties such as overcharging and traffic congestion, reducing its effectiveness. To solve these issues, we propose implementing an advanced toll collection system that includes GPS technology, an ANPR camera, and a variety of machine learning algorithms. This new method will transform toll collecting by properly calculating toll charges based on the actual distance traveled by vehicles on the highway, removing the need for drivers to stop at toll booths. The system uses GPS technology to precisely compute the distance traveled by each car, while ANPR cameras guarantee the journey's validity. As a result, vehicles will only be paid for what distance they have traveled, reducing overcharging concerns and congestion at toll gates. Our major goal is to create a smooth and efficient tolling mechanism that overcomes the limitations of traditional tollgate systems. We hope to improve the toll collection process, shorten wait times, and reduce the consumption of fuel associated with long lines at toll booths by utilizing cutting-edge technologies. By applying this innovative strategy, we hope to improve not only toll collecting efficiency but also commuters' overall driving experience. © 2024 IEEE.

关键词： Traffic congestion

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Multilingual Language Detection and Translation System with Multinomial Naive Bayes 5

Multilingual Language Detection and Translation System with ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Vuddanti, Sowjanya Ariveni, Dhathri Sri Lekha Kethepalli, Niharika Sri Sandhya Manchimi, Lokesh Yajjavarapu, Jaswanth Bali Reddy College of Engineering Department of Artificial Intelligence and Data Science Lakireddy Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350367171

Smooth communication becomes essential as the world unites different linguistic groupings. The proposed system for language recognition and translation addresses this difficulty. The proposed system uses a Google Translate-powered 'bridge' with a machine learning 'detective' to properly identify text language and translate between any supported combination. The Multinomial Naive Bayes classifier, a machine learning approach, is used by the language detection component to determine the language of input text. As the 'detective,' the Multinomial Naive Bayes classifier correctly determines the language of the input text with a remarkable accuracy rate of 97 %. The model can accurately classify language in a range of languages by preprocessing the text data and generating a representation of words in a bag. To translate text between languages, the text translation component uses the Google Translate API via the Google library. With the flexibility to choose the source and target languages provided by this component, users can translate text from any supported language to the language of their choice. Easy to use, it enables people to promote intercultural understanding and overcome language barriers in social media, business, education, and other domains, making the world more inclusive and connected. © 2024 IEEE.

关键词： Machine translation

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