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SSRN 2023年

作者： Zhang, Xinzhi Zhu, Qingling Lin, Qiuzhen Chen, Wei-Neng Li, Jianqiang Coello, Carlos Artemio Coello College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computers South China University of Technology Guangzhou510006 China Department of Computer Science MexicoD.F. 07360 Mexico Ikerbasque Spain

The Nurse Rostering Problem (NRP) is important for hospital management, which aims to balance both the needs of employees and the requirements of hospital operations. Currently, most studies often use local search methods with adaptive neighborhood search (ANS) or variable neighborhood descent (VND) to efficiently tackle the NRP. However, the use of ANS or VND in their local search strategies typically focuses on optimizing the current solution without considering the potential long-term consequences of these local improvements, which may easily fall into local optima and resultantly limit the final performance. Thus, this paper proposes a Deep Q-network-based Neighborhood Tabu Search (called DQN-NTS) method for tackling the NRP. Our method is a first attempt to train a deep Q-network (DQN) as a selection method for identifying effective sequences of neighborhoods, which can learn good transitions among the neighborhoods. We model the neighborhood search as a Markov model, where the current solution represents the state, and the reward signifies the improvement in the objective function. This approach enables DQN to consider neighborhood transition sequences that have historically obtained superior solutions, thereby helping to avoid local optima and achieve global optima by considering long-term impacts. Moreover, tabu search is combined with the DQN to iterate better solutions and diversify the search space by changing the weights of nurses when stagnating at the local optimum. Experimental results indicate that DQN-NTS outperforms other state-of-the-art tabu search methods with ANS and VND when running on the Second International Nurse Rostering Competition dataset. © 2023, The Authors. All rights reserved.

关键词： Tabu search

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Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

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Front-end Control Mechanism of Electronic Records

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computer Systems Science & engineering 2021年第12期39卷 337-349页

作者： Jiang Xu Ling Wang Xinyu Liu Xiujuan Feng Yongjun Ren Jinyue Xia School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Mines China University of Mining and TechnologyXuzhou221116China International Business Machines Corporation(IBM) NYUSA

In the digital era,how to ensure the authenticity and integrity of elec-tronic records has become an open challenging ***-end control is an important concept as well as a basic principle in electronic record *** the instruction of front-end control,many original management links in the record-management stage are required to move forward,and the managers enter the formation stage of the electronic records to ensure the ***-ever,the front-end control technique primarily focuses on transaction manage-ment,and it lacks the strategy of providing the control of electronic *** this paper,a novel electronic record front-end control mechanism is proposed by adopting proxy re-encryption and requiring archivists to participate in the man-agement of electronic records before the record is created to solve the ***,when an electronic record is generated,the proposed mechanism interacts with the producer of the electronic record to generate a corresponding encryption ***,electronic records are encrypted by the key to protect their confidentiality,which can prevent the leakage of electronic record *** addition,when transferring the electronic record,archivists use proxy re-encryption technology to convert electronic records,allowing management by an archivist,ensuring their originality and authenticity.

关键词： Electronic record front-end control proxy re-encryption

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AI Personalized Mental Health Monitoring System using Machine Learning, and Natural Language Processing

AI Personalized Mental Health Monitoring System using Machin...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Mohamed Jamal Irfan G. Dhivya N. Raghavendran M. C. Babu T. Jasperline R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Dr. G. U. Pope College of Engineering Tuticorin Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study introduces an AI-driven customized mental health monitoring system that uses machine learning (ML) and natural language processing (NLP) to provide ongoing mental health evaluation and assistance. The system uses user-generated textual data, including journal entries, chat logs, and social media postings, to assess emotional states, stress levels, and mental health issues. The system uses sophisticated natural language processing methods to identify linguistic patterns associated with mental health problems such as depression, anxiety, and others. The machine learning algorithms customize monitoring by adjusting to the unique characteristics of each user and use past data to provide customized suggestions and preemptive alerts. The technology aims to enhance human interaction and function as a proactive tool in mental health treatment by providing real-time information. The system extracts emotional indicators from textual input using sentiment analysis methods and pre-trained natural language processing models such as BERT. The results are used with user demographics and history to build machine learning classifiers, such as Random Forest (RF) and Support Vector Machine (SVM), which predict mental health disorders. Supervised learning is used to develop the model, and suggestions are progressively improved using a tailored feedback system. The SVM and RF algorithms demonstrated an 88% and 92%accuracy rate in forecasting mood swings and early warning indicators of mental health disorders when evaluated on a set of anonymized mental health information. The 30% increase in user involvement demonstrated the efficacy of tailored suggestions in proactive mental health treatment.

关键词： Support vector machines Machine learning algorithms Accuracy Social networking (online) Supervised learning Mental health Natural language processing Monitoring Random forests Classification tree analysis

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Student-Teacher Feature Pyramid Matching for Anomaly Detection 32

Student-Teacher Feature Pyramid Matching for Anomaly Detecti...

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32nd British Machine Vision Conference, BMVC 2021

作者： Wang, Guodong Han, Shumin Ding, Errui Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Vision Technology Baidu Inc. Beijing China

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones. © 2021. The copyright of this document resides with its authors.

关键词： Anomaly detection

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A Framework Design of ML Classifier Algorithm for Retrieve the Information About the Drugs and its Quality

A Framework Design of ML Classifier Algorithm for Retrieve t...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： M Meenakshi S. Ruban T J Nandhini S Loganayagi Raad Muhammed Sayed Enas Hassan Imad Al-Din Hussein Al-Saidi Saveetha School of Engineering Saveetha University Thandalam Chennai department of software technology St aloysius deemed to be university Department of computer science and engineering saveetha school of engineering simats saveetha university chennai india department of computer science and engineering saveetha school of engineering(simats) thandalam chennai tamil nadu india collage of nursing national university of science and tqechnology dhi qar iraq Al-Farahidi University Baghdad Iraq Computer Engineer Department al-kunooze university college basra iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results—the top model has an average accuracy of about $85 \%$—the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible.

关键词： Training Machine learning algorithms Soft sensors Machine learning Production Manuals Predictive models

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An Adaptive Molecular Docking Optimization Model with Multiple Scoring Functions 3

An Adaptive Molecular Docking Optimization Model with Multip...

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3rd IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2020

作者： Chen, Qiguo Wan, Biao Wuhan Polytechnic College of Computer Technology and Software Engineering Wuhan430074 China

ISBN: (纸本)9781728183039

With the development of molecular biology and structural biology, people have a deeper understanding of the interaction between physiologically active molecules and biological macromolecules. The development of molecular biology and structural biology has led to the clarification of the function of some target biological macromolecules and the determination of three-dimensional structures, which further promotes the development and application of drug design methods. Therefore, this paper proposes an adaptive drug molecular docking algorithm based on multiple scoring functions. There are three scoring functions of the algorithm, namely base experience and knowledge based, and force field-based functions. An adaptive molecular docking optimization model is established by the combination of three functions and the relationship between hydrophobic entropy. The simulation results show that the evaluation results of this method are more accurate and comprehensive. © 2020 IEEE.

关键词： Molecular biology

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Disparity Analysis Between the Assembly and Byte Malware Samples with Deep Autoencoders

Disparity Analysis Between the Assembly and Byte Malware Sam...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Muhammed Amin Abdullah Yongbin Yu Jingye Cai Yakubu Imrana Nartey Obed Tettey Daniel Addo Kwabena Sarpong Bless Lord Y. Agbley Benjamin Appiah School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Ho Technical University Ho Ghana

ISBN: (纸本)9781665493901

Malware attacks in the cyber world continue to increase despite the efforts of Malware analysts to combat this problem. Recently, Malware samples have been presented as binary sequences and assembly codes. However, most researchers focus only on the raw Malware sequence in their proposed solutions, ignoring that the assembly codes may contain important details that enable rapid Malware detection. In this work, we leveraged the capabilities of deep autoencoders to investigate the presence of feature disparities in the assembly and raw binary Malware samples. First, we treated the task as outliers to investigate whether the autoencoder would identify and justify features as samples from the same family. Second, we added noise to all samples and used Deep Autoencoder to reconstruct the original samples by denoising. Experiments with the Microsoft Malware dataset showed that the byte samples' features differed from the assembly code samples.

关键词： Training Codes Noise reduction Information processing Media Malware Data models

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Design of Face Recognition Attendance 5

Design of Face Recognition Attendance

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5th IEEE International Conference on Image, Vision and Computing, ICIVC 2020

作者： Lee, Hai-Wu Gu, Wen-Tan Wang, Yuan-Yuan Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian City China

ISBN: (纸本)9781728166612

In recent years, face recognition technology has developed rapidly, and its application range has become more and more extensive. It is one of the most important application fields in computer vision technology. However, there are still many technical factors that restrict the application and promotion of face recognition technology. For example: shadows, occlusions, light and dark areas, dark light, highlights and other factors will make the face recognition rate drop sharply. Therefore, face recognition has extremely high research and application value. We use the Local Binary Patterns (LBP) algorithms with histogram equalization to obtain high-resolution images and improve the recognition rate in different scenarios, and try to apply face recognition to attendance. © 2020 IEEE.

关键词： Face recognition

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Improving Generalization in Meta-Learning Via Meta-Gradient Augmentation

SSRN

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SSRN 2023年

作者： Wang, Ren Sun, Haoliang Lin, Yuxiu Wei, Qi Yin, Yilong School of Software Shandong University Jinan250101 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan250014 China School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue 639798 Singapore

Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new tasks. Existing schemes solve it by enhancing the mutual-exclusivity or diversity of training samples, but these data manipulation strategies are data-dependent and insufficiently flexible. This work alleviates overfitting in meta-learning from the perspective of gradient regularization and proposes a data-independent Meta-Gradient Augmentation (MGAug) method. The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop. Specifically, we explore three pruning strategies, including random width pruning, random parameter pruning, and a newly proposed catfish pruning that measures a Meta-Memorization Carrying Amount (MMCA) score for each parameter and prunes high-score ones to break rote memories as much as possible. The proposed MGAug is theoretically guaranteed by the generalization bound from the PAC-Bayes framework. Extensive experiments on multiple few-shot learning benchmarks validate MGAug’s effectiveness and significant improvement over various meta-baselines. The code is publicly available at https://***/xxLifeLover/MGAug. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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