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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Ruchika Nayak, Debasish Swapnesh Kumar Rout, Chinmayee Priyadarshini Jena, Lambodar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India Nalanda Institute of Technology Department of Computer Application Bhubaneswar India Siksha 'O Anusandhan University Department of Data Science Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9798350370188

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

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computers, Materials & Continua 2025年第1期82卷 913-928页

作者： Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang New Engineering Industry College Putian UniversityPutian351100China Engineering Research Center of Big Data Application in Private Health Medicine(Putian University) Fujian Province UniversityPutian351100China Computer College Guangdong University of Science&TechnologyDongguan523668China

In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关键词： Polyp detection YOLOv7 SPD-Conv CBAM deconvolution

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Balancing data imbalance in biomedical datasets using a stacked augmentation approach with STDA, DAGAN, and pufferfish optimization to reveal AI's transformative impact

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International Journal of Information Technology (Singapore) 2025年第1期17卷 455-480页

作者： Veedhi, Bhaskar Kumar Das, Kaberi Mishra, Debahuti Mishra, Sashikala Behera, Mandakini Priyadarshani Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed to Be) University Maharashtra Pune India School of Computer Engineering KIIT (Deemed to Be) University Odisha Bhubaneswar India

This study systematically assesses the effectiveness of data augmentation techniques, focusing particularly on the stacked approach, across a diverse range of biomedical datasets. Advanced data augmentation methods, including style transfer data augmentation (STDA) and data augmentation generative adversarial network (DAGAN), offer promising solutions in this context. STDA leverages style transfer techniques from computer vision to create varied and realistic synthetic samples while preserving semantic content, thereby enhancing dataset variability and improving model generalization. Conversely, DAGAN utilizes generative adversarial networks (GANs) to learn the underlying data distribution, resulting in the generation of high-quality synthetic samples. Subsequently, STDA and DAGAN techniques are applied to the selected data, followed by convolution and pooling operations on the augmented samples. The pooled samples are then stacked, and the Cosine similarity is calculated to measure the resemblance to the original dataset. Parameters of STDA and DAGAN are fine-tuned to maximize the Cosine similarity. Additionally, a metaheuristic optimization algorithm called Pufferfish optimization algorithm (POA) is employed to optimize decision variables—learning rate, number of hidden units (common for both STDA and DAGAN), number of attention heads for STDA, and latent space dimensionality of DAGAN. The classification performance of the proposed POA-stacked STDA-DAGAN data augmentation methodology, along with variants using genetic algorithm (GA) and particle swarm optimization (PSO), is compared with original datasets and SMOTE balanced datasets using various classifiers and the observed accuracies, sensitivity, specificity and F-score are ranging from 91.83% to 99.11%, 91.38% to100%, 92.50% to 100% and 92.95% to 98.32% respective for all ten biomedical datasets used in this research. Additionally, the class distribution graphs, learning curves, statistical validation, and exe

关键词： Biomedical data Data augmentation Data augmentation generative adversarial network (DAGAN) Pufferfish optimization algorithm (POA) Style transfer data augmentation (STDA)

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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A Survey of Current Progress in Depression Detection Using Deep Learning and Machine Learning

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Biomedical Materials and Devices 2025年 1-25页

作者： Kumari, Meena Singh, Gurpreet Pande, Sagar Dhanraj Computer Application Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Department of Computer Science and Engineering Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune 412106 India

Generally, depression is the most prevalent mental disorder, which has affected hundreds of people around the world and causes a high risk of committing suicide. Automatic diagnosis of depression detection plays a vital role in examining people’s mental illness through various Artificial Intelligence (AI) techniques and has a high probability of reducing patient’s burden by early treatments. In this research, the crucial area of depression detection is examined, with an emphasis on the crucial function of hybridized algorithms. More studies have been developed worldwide to provide a review of depression detection techniques. Still, obtaining significant information about the input data and the different mechanisms utilized in each research for depression detection is challenging. Consequently, this review presents a systematic review of various Deep Learning (DL) and Machine Learning (MI) techniques based on the input data utilized in the models for detecting depression, heralding a seismic shift in our understanding of depression and the diagnostic and support systems connected to it. More specifically, this review examines the literature from 50 recent research articles based on depression detection topics incorporated with its significant morphology that helps findings in the creation of novel models in the future. Further, this review discusses the research gaps, challenges, dataset collection, interdisciplinary cooperation, other ideal strategies, and future scope for progressing and catalyzing the research in this domain. In addition, this review presents an overview to identify the objectives and restrictions of different literature works presented in the domain of depression detection. Moreover, the developed review assists in advancing the knowledge about the technologies adopted for depression detection and serves as a suitable reference for upcoming researchers working in this domain. © The Author(s), under exclusive licence to Springer science+Business M

关键词： Deep learning Depression detection Machine learning Multimodal data Natural language processing

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Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

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Machine Intelligence Research 2024年第1期21卷 89-103页

作者： Chaojie Wu Mingyang Li Ying Gao Xinyan Xie Wing W.Y.Ng Ahmad Musyafa School of Computer Science and Engineering South China University of TechnologyGuangzhou 511442China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's HospitalGuangzhou 510180China

The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every *** art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright ***,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level *** alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level ***,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic ***,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance ***,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization *** experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.

关键词： Weakly supervised object localization erasing method deep learning computer vision art authentication and copyright protection

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Transformative Insights: Harnessing Machine Learning for Big Data Analytics 3

Transformative Insights: Harnessing Machine Learning for Big...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Bhatt, Chandradeep Sagta, Divas Chauhan, Rahul Dhondiyal, Shiv Ashish Sharma, Sanjay Baloni, Dev Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Quantum University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

Like oil in the past, data has become a very important resource in the modern world. But just as oil must be refined in order to be valued, data must also be handled and evaluated in order to yield worthwhile insights. Big Data analytics is a discipline that has developed in response to the expanding demand for efficient data analysis and interpretation. In order to find hidden patterns, connections, and insights, it involves examining huge, complicated datasets utilizing cutting-edge tools and methods. Big Data analytics' importance stems from its capacity to decipher the secrets contained in any dataset and glean insightful information. When appropriately analyzed and addressed, these insights can produce beneficial results. Big Data Analytics is extremely important in the context of consumer behavior. This technology is being used by marketers more frequently to analyze customer behaviors more accurately. Marketers can better understand their target market's preferences, demands, and buying habits by analyzing customer behavior. With the use of this information, marketers can make more informed choices and modify their marketing plans to specifically address customer needs. In the end, this may help to improve firm performance outcomes including boosted sales, enhanced client contentment, and general expansion. The aforementioned study seeks to offer a thorough overview of Big Data technology, including its goal, importance, and potential applications. It also looks into how Big Data technologies affect customer behavior and overall business effectiveness. The goal of this study is to shed light on these elements and the potential advantages and consequences of applying big data analytics across different fields. © 2023 IEEE.

关键词： Big Data Data Analytics Machine Learning RDBMS

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Multi-Label MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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