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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Binon Teji Swarup Roy Pietro Hiram Guzzi Dinabandhu Bhandari Department of Computer Applications Network Reconstruction & Analysis (NETRA) Lab Sikkim University Gangtok India School of Computer Science and Biomedical Engineering University of Catanzaro Italy Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models’ qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics.

关键词： Analytical models Accuracy Systematics Reviews Biological system modeling Computational modeling Neural networks Predictive models Biology Organisms

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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Neighborhood co-occurrence modeling in 3D point cloud segmentation

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Computational Visual Media 2022年第2期8卷 303-315页

作者： Jingyu Gong Zhou Ye Lizhuang Ma Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai CLS Fintech Co. LTDShanghai 200030China MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong UniversityShanghai 200240China

A significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point ***,co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current *** this paper,we propose a neighborhood co-occurrence matrix(NCM)to model local co-occurrence relationships in a point *** generate target NCM and prediction NCM from semantic labels and a prediction map ***,Kullback-Leibler(KL)divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence ***,for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly,we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction *** integrate our method into an existing backbone and conduct comprehensive experiments on three datasets:Semantic3D for outdoor space segmentation,and S3DIS and ScanNet v2 for indoor scene *** indicate that our method can significantly improve upon the backbone and outperform many leading competitors.

关键词： 3D vision point cloud co-occurrence relation modeling semantic segmentation

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PAKE - PoS Tagger Augmented Keyword Extraction

PAKE - PoS Tagger Augmented Keyword Extraction

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

作者： Kohinoor, Md Saidur Rahman Miah, Ayesha Loylus Ali, Shah Fayez Hossain, Md Sabir Bijoy, Md. Hasan Imam Sakib, Shadman King Fahd U. of Petroleum and Minerals Information and Computer Science Dhahran31261 Saudi Arabia InteX Research Lab Ai and Nlp Discipline Sylhet3100 Bangladesh Daffodil International University Computer Science and Engineering Dhaka1216 Bangladesh University of Maryland Baltimore County Department of Information Systems MD21250 United States

ISBN: (纸本)9798331529765

The state-of-the-art techniques for automatic keyword extraction majorly deal with the collection of long documents. However, for several reasons, these do not provide satisfactory results for shorter lengths of documents. Moreover, with the ever-increasing amounts of information available, a keyword extraction system that automatically deals with varying lengths of text can lessen the workload and make the entire process of manually assigning the keywords less time-consuming. For this, the widely used Natural Language Processing (NLP) techniques are examined in the context of extensive data. Therefore, this research introduces PAKE - PoStagger Augmented Keyword Extraction system as a practical amalgamation of statistical and textual-based features based on an unsupervised key phrase extracting algorithm to stand out as a suitable alternative to the existing solutions. The effectiveness is demonstrated by comparing it with six state-of-the-art unsupervised methods, and the results are illustrated using four datasets. © 2024 IEEE.

关键词： Natural language processing systems

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Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation

Exploitation or Exploration Next? User Behavior Decoupling a...

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IEEE International Conference on Data Mining (ICDM)

作者： Nengjun Zhu Lingdan Sun Xiangfeng Luo Jian Cao Qi Zhang Xinjiang Lu School of Computer Engineering and Science Shanghai University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science Tongji University Shanghai China Business Intelligence Lab Baidu Research Beijing China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Recent trends in next-item recommendation systems have focused on modeling user intents. Traditional methods often extract users' inherent intents from the most representative items in a session, overlooking “unexpected items” that deviate from the majority in various contextual aspects. These unexpected items, frequently present, can be crucial indicators of a user's inclination towards exploring new options, signaling emerging intents that warrant significant attention. In response, we introduce DbMei, a novel approach that decouples user behaviors and emphasizes the modeling of emerging intents. DbMei distinguishes between two user behavior types: “focused shopping”, which aligns with users' inherent intents, and”wandering shopping”, which aligns with emerging intents. Focused shopping is analyzed using topic modeling and hypergraph learning while wandering shopping is explored through session neighbor retrieval. An exploitation-exploration mechanism is employed to determine the behavioral probability distribution for upcoming items. This integrated modeling of focused and wandering shopping behaviors drives our recommendation process. Extensive empirical studies on two real-world datasets, Amazon-KDD and Beauty, showcase DbMei's superiority over leading methods regarding Recall and MRR metrics. Our code is publicly available at https://***/sunlingdan-123/DbMei.

关键词： Measurement Analytical models Codes Market research Data models Probability distribution Data mining Recommender systems Context modeling

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Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality

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Spatial Information Research 2023年第4期31卷 399-407页

作者： Subbulakshmi, P. Vimal, S. Robinson, Y. Harold Verma, Amit Nayak, Janmenjoy School of computer science and Engineering VIT Chennai campus Tamilnadu India Data Analytics Lab Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Computer Science & Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha India

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

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An Advanced Liver Disease Detection Tool with a Stacking-Ensemble-based Machine Learning Approach

An Advanced Liver Disease Detection Tool with a Stacking-Ens...

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IEEE International Conference on Big Data

作者： Md. Shazzad Hossain Shaon Md. Fahim Sultan Tasmin Karim Alfredo Cuzzocrea Mst Shapna Akter Department of Computer Science and Engineering Oakland University Rochester MI USA iDEA Lab University of Calabria Italy Department of Computer Science University of Paris City Paris France

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Liver diseases (LD) encompass a variety of disorders associated with the liver, including infectious hepatitis, obesity, cirrhosis, and malignancy, which constitute significant health issues in the world. Due to the minimal symptoms, comprehending the disease becomes extremely difficult until its severe stages; earlier detection is advantageous for appropriate action, which could save lives. This study developed a StackLD framework based on the stacking-ensemble-based machine learning approach. In the preliminary phase, we collected the Indian Liver Patient Dataset (ILPD) dataset, which contains 11 features, and the dataset highlighted a significant discrepancy. To overcome this, we used the SMOTE to rebalance the dataset, facilitating the development of robust machine learning models. We applied 7 different models such as XGB, LGBM, DT, KNN, RF, KNN, and stacking approaches with several evaluation metrics on independent test methods The analysis presented that the stacking technique executed superior in accuracy, sensitivity, specificity, and area under the curve, with values of 0.8622, 0.8933, 0.8369, and 0.9275, respectively. These outcomes indicate that our approach effectively differentiates between positive and negative classes. This study illustrates that the Alkphos, Sgot, and Sgpt elements have a significant role in determining liver disease (LD) from various features. As a result, a web server was built using these attributes, demonstrating that our model accurately predicts liver disorders at an early stage which is useful insight into the medical field with the potential to improve diagnostic procedures and patient outcomes.

关键词： Analytical models Liver diseases Stacking Machine learning Predictive models Nearest neighbor methods Data models Medical diagnostic imaging Diseases Classification tree analysis

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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TOWARDS DIVERSE EVALUATION OF CLASS INCREMENTAL LEARNING: A REPRESENTATION LEARNING PERSPECTIVE 3

TOWARDS DIVERSE EVALUATION OF CLASS INCREMENTAL LEARNING: A ...

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3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Cha, Sungmin Kwak, Jihwan Shim, Dongsub Kim, Hyunwoo Lee, Moontae Lee, Honglak Moon, Taesup Computer Science Department Courant Institute of Mathematical Sciences New York University United States Department of Electrical and Computer Engineering Seoul National University Korea Republic of Advanced Machine Learning Lab LG AI Research Korea Republic of Zhejiang Lab China Department of Information and Decision Sciences University of Illinois Chicago United States ASRI INMC IPAI AIIS Seoul National University Korea Republic of

Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes. The common evaluation protocol for CIL algorithms is to measure the average test accuracy across all classes learned so far - however, we argue that solely focusing on maximizing the test accuracy may not necessarily lead to developing a CIL algorithm that also continually learns and updates the representations, which may be transferred to the downstream tasks. To that end, we experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning and propose new analysis methods. Our experiments show that most state-of-the-art algorithms prioritize high stability and do not significantly change the learned representation, and sometimes even learn a representation of lower quality than a naive baseline. However, we observe that these algorithms can still achieve high test accuracy because they enable a model to learn a classifier that closely resembles an estimated linear classifier trained for linear probing. Furthermore, the base model learned in the first task, which involves single-task learning, exhibits varying levels of representation quality across different algorithms, and this variance impacts the final performance of CIL algorithms. Therefore, we suggest that the representation-level evaluation should be considered as an additional recipe for more diverse evaluation for CIL algorithms. © 2024 CoLLAs. All Rights Reserved.

关键词： Contrastive Learning

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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