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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Sammak, Musabbir Hasan Arman, Md Shohel Jahan, Hasnur Mahmud, Imran Alam, Md. Rittique Biplob, Khalid Been Md. Badruzzaman Daffodil International University DIU Data Science Lab Department of Software Engineering Dhaka Bangladesh

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

One of the worst diseases in the world is heart disease or cardiovascular disease. Heart disease is on the rise globally, and doctors are becoming quite concerned about it. Therefore, it's crucial to accurately identify heart disease kinds when you require them. The complete understanding of cardiac disease has become one of the most popular scientific topics. In order to determine the best feature for accurately identifying heart disease, we worked with the "statlog"dataset. There, we trained our model using the LightGMB Python module. It is the Boosting Machine framework. We utilized the auROC score together with a 5-fold cross-validation for performance measurements. Figure 3 displays the results on the test data, which were 0.85 auROC with a 95% confidence interval of 0.70-0.99. However, it obtained 0.81 auPRC with a 95% confidence interval of 0.59 to 0.99. It obtained recall scores of 91.83% and precision scores of 77.52%. Shapely Additive exPlanations or shape value is utilized to discover the best features in this case. We categorize our heart disease datasets by identifying the most effective characteristics. As a consequence, it will assist medical professionals in identifying cardiac disease through practical application and help save countless lives © 2022 IEEE.

关键词： Diseases

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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A data Mining Approach to Finding Face Mask from Bangladeshi News Channel 13

A Data Mining Approach to Finding Face Mask from Bangladeshi...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Arman, Md Shohel Alam, Md. Rittique Jahan, Hasnur Islam, Lameya Sammak, Musabbir Hasan Biplob, Khalid Been Md. Badruzzaman Daffodil International University DIU Data Science Lab Department of Software Engineering Dhaka Bangladesh

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Face Mask Detection is currently a hot topic that has piqued the interest of researchers all over the world. Today, the entire world is dealing with the COVID-19 pandemic. To control the spread of the Coronavirus the most important task people need to do is use a mask. There is still a lot of research and study being done on COVID-19. Several studies have also shown that wearing a face mask significantly reduces the problem of viral transmission. In addition, a person wearing a face mask perceives a sense of protection. When we are at home, we take care of everything, but when we are in public places such as offices, malls, and colleges, it becomes more difficult to keep people safe. Machine Learning and data Mining are a collection of technologies that provide effective solutions to complex problems in a variety of fields. We attempted to develop a face mask recognition system using machine learning in order to prevent the spread of the Coronavirus. This is a good system for detecting a face mask in news channel images and videos. It can recognize both Mask and No Mask faces. With the advancement of this system, it will be possible to detect whether or not a person is wearing a face mask. If the person is not wearing a face mask, it will display a message such as "No Mask,"otherwise it will display "Mask Detected." © 2022 IEEE.

关键词： data mining

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning 31

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhai, Jiaqi Shi, Xuanhua Huang, Kaiyi Ye, Chencheng Hu, Weifang He, Bingsheng Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore

ISBN: (纸本)9798331506476

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication. © 2025 IEEE.

关键词： Embeddings

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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi label Image Classification

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Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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Towards Instruction-Tuned Verification for Improving Biomedical Information Extraction with Large Language Models

Towards Instruction-Tuned Verification for Improving Biomedi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jin Yuan, Ruifan Tian, Yu Li, Jingsong Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Zhejiang University College of Biomedical Engineering & Instrumental Science Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350386226

Biomedical information extraction, including tasks like Named Entity Recognition (NER) and Relationship Extraction (RE), plays a critical role in analyzing unstructured biomedical data, facilitating more accurate downstream applications such as clinical decision support and personalized medicine. Although Large Language Models (LLMs) excel in various domains, they often struggle with issues like hallucinations and domain-specific inaccuracies, limiting their effectiveness in biomedical applications. To tackle these challenges, we propose a novel framework that incorporates an external dual-stage verifier to enhance biomedical information extraction. This verifier is prompted to first identify and complete missing entities and relations, and then filter out incorrect extractions, thereby enhancing both precision and recall in information extraction. Furthermore, we proposed an approach for constructing the instruction-tuning dataset using both positive and negative demonstrations, which improves the verifier's correction capabilities through Parameter-Efficient Fine-Tuning (PEFT). Our extensive experiments on three public benchmark datasets cover both biomedical NER and RE tasks, comparing four LLMs ranging from 7 to 175 billion parameters to validate the effectiveness and scalability of our approach. The results demonstrate that by integrating an external verifier specifically finetuned for biomedical tasks, we achieve substantial improvements in precision, recall, and F1 scores for NER and RE tasks. Our method consistently outperforms In-Context Learning (ICL) approaches in F1 scores, achieving increases of up to 20% in some cases. Furthermore, ablation studies demonstrate the effectiveness of both stages in the verification process. The proposed framework provides valuable insights for accurate biomedical information extraction using LLMs, highlighting its potential applications in real-world clinical settings. © 2024 IEEE.

关键词： Personalized medicine

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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A Four-Valued Ambiguous Logic: Application in Designing Ambiguous Inference System for Control Systems

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International Journal of Fuzzy Systems 2023年第8期25卷 2921-2938页

作者： Singh, Pritpal Huang, Yo-Ping Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Ambiguous set has recently been introduced as a new branch of mathematics and computer science dealing with the nature of ambiguity of uncertain events. There is much scope for the development of new theories based on the principles of the ambiguous set. In this direction, an idea of ambiguous logic is proposed, which can be regarded as a family of four-valued logic. In this study, the limitations of existing theories such as fuzzy logic, intuitionistic fuzzy logic, and neutrosophic logic that are used to deal with uncertainty are first discussed. Then an introduction to the ambiguous set and ambiguous logic is given. In ambiguous logic, there are four membership degrees for each proposition, which can be characterized as true, false, true-ambiguous, and false-ambiguous. We then analyze the membership degrees of propositions using various connectives, including negation, conjunction, and disjunction. In this study, we discuss various laws that support the different algebraic operations for comparing propositions in terms of membership degrees. An approach is presented to define a rule using a proposition consisting of membership degrees, called an ambiguous rule. The modus ponens rule of inference is defined by combining various ambiguous rules, and is called ambiguous inference. Finally, to demonstrate the real-time application of ambiguous logic, an ambiguous inference system is designed that can be used in machine control systems. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Fuzzy logic

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Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

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Computers, Materials & Continua 2023年第4期75卷 293-309页

作者： Mingshuai Sheng Jingbing Li Uzair Aslam Bhatti Jing Liu Mengxing Huang Yen-Wei Chen School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan

Medical images are used as a diagnostic tool, so protecting theirconfidentiality has long been a topic of study. From this, we propose aResnet50-DCT-based zero watermarking algorithm for use with medicalimages. To begin, we use Resnet50, a pre-training network, to draw out thedeep features of medical images. Then the deep features are transformedby DCT transform and the perceptual hash function is used to generatethe feature vector. The original watermark is chaotic scrambled to get theencrypted watermark, and the watermark information is embedded into theoriginal medical image by XOR operation, and the logical key vector isobtained and saved at the same time. Similarly, the same feature extractionmethod is used to extract the deep features of the medical image to be testedand generate the feature vector. Later, the XOR operation is carried outbetween the feature vector and the logical key vector, and the encryptedwatermark is extracted and decrypted to get the restored watermark;thenormalized correlation coefficient (NC) of the original watermark and therestored watermark is calculated to determine the ownership and watermarkinformation of the medical image to be tested. After calculation, most ofthe NC values are greater than 0.50. The experimental results demonstratethe algorithm’s robustness, invisibility, and security, as well as its ability toaccurately extract watermark information. The algorithm also shows goodresistance to conventional attacks and geometric attacks.

关键词： Medical images deep residual network resnet50-DCT privacy protection robustness security

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Real-time road occupancy and traffic measurements using unmanned aerial vehicle and fundamental traffic flow diagrams

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Personal and Ubiquitous Computing 2023年第5期27卷 1669-1680页

作者： Ahmed, Afzal Outay, Fatima Farooq, Muhammad Umer Saeed, Sehrish Adnan, Muhammad Ismail, Muhammad Ali Qadir, Adnan Department of Urban & Infrastructure Engineering NED University of Engineering & Technology Karachi Pakistan Zayed University Dubai United Arab Emirates Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan Exascale Open Data Analytics Lab NED University of Engineering & Technology Karachi Pakistan Hasselt University Diepenbeek Belgium

The technologies and sensors developed for standard traffic streams often fail to accurately measure the heterogeneous traffic with no lane discipline. This research proposes an efficient framework to measure traffic flow parameters in real-time using an unmanned aerial vehicle (UAV). The proposed framework in this research estimates road area occupancy from the images extracted from the video recorded using a UAV by applying the image segmentation technique. The road area occupancy is converted into traffic density using an occupancy-density model developed for the local heterogeneous traffic. A speed-density fundamental diagram is developed, which estimates the average speed corresponding to the measured traffic density. The fundamental relation between flow rate, average speed, and density is applied to compute traffic flow rate using the estimated average speed and density. The proposed framework is computationally less demanding in comparison with other computer vision and artificial intelligence–based techniques. The proposed framework accurately measured road area occupancy with an average accuracy of 97.6%. Traffic density was measured with an average accuracy of 88.8%. Similarly, the average speed and flow rate were measured with an average accuracy of 85.4% and 84.2%, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Image segmentation

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