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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of Information Technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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Compressing Distributed Network Sketches With Traffic-Aware Summaries

Compressing Distributed Network Sketches With Traffic-Aware ...

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作者： Harris, Dor Rinberg, Arik Rottenstreich, Ori Technion Department of Computer Science Haifa32000 Israel Technion Department of Computer Science The Department of Electrical and Computer Engineering Haifa32000 Israel

Network measurements are important for identifying congestion, DDoS attacks, and more. To support real-time analytics, stream ingestion is performed jointly by multiple nodes, each observing part of the traffic, periodically reporting its measurements to a single centralized server that aggregates them. To avoid communication congestion, each node reports a compressed version of its collected measurements. Traditionally, nodes symmetrically report summaries of the same size computed on their data. We explain that to maximize the accuracy of the joint measurement, nodes should imply various compression ratios on their measurements based on the amount of traffic observed by each node. We illustrate the approach for three common sketches: The Count-Min sketch (CM), which estimates flow sizes as well as for the K-minimum-values (KMV) sketch and the HyperLogLog (HLL), which both estimate the number of distinct flows. For each sketch, we compute node compression ratios based on the traffic distribution. In general, this is done with a single round of communication with the central server, after which the compression ratio for each node can be computed. We perform extensive simulations for the sketches and analytically show that, under real-world scenarios, our sketches send smaller summaries than traditional ones while retaining similar error bounds. © 2004-2012 IEEE.

关键词： Uncertainty analysis

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A Low-Cost Pipelined Architecture Based on a Hybrid Sorting Algorithm

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 717-730页

作者： Chen, You-Rong Ho, Chien-Chia Chen, Wei-Ting Chen, Pei-Yin The Digital Integrated Circuit Design Laboratory Department of Computer Science and Information Engineering National Cheng Kung University Tainan70101 Taiwan MediaTek Inc. Hsinchu30078 Taiwan

In this paper, a low-cost pipelined architecture based on a hybrid sorting algorithm is proposed. The proposed architecture is constructed with a bitonic sorter and several cascaded bidirectional insertion sorting units. The bidirectional insertion sorting unit uses the segmented sorted subsequence generated by the bitonic sorter as input, and records the maximum and minimum values of the subsequence. After all segmented subsequences are processed through the cascaded bidirectional insertion sorting units, a sorted sequence is obtained. The proposed architecture is implemented using the Verilog hardware description language (HDL) and synthesized using the Synopsys Design Compiler with a TSMC 90-nm cell library. The experimental results indicate that the proposed architecture can not only shorten sorting cycles but also reduce hardware area costs. Moreover, sorting cycles can be further shortened by increasing the parallelism of the proposed architecture. Under the configuration that 2048 32-bit data to be sorted and 16 data have to be processed simultaneously, the proposed architecture can improve the throughput-to-gate-count ratio by 16%, and throughput-to-power-consumption-ratio by 25% compared to the existing sorting design. The proposed architecture makes the most efficient use of hardware resources. © 2023 IEEE.

关键词： Costs

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A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks

A Comprehensive Research on Deep Learning Based Routing Opti...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Gaurav Girisha, G.S. Shamanth, N. Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. © 2023 IEEE.

关键词： Controller attacks Deep Learning Deterministic Policy Gradient Optimal routing Software defined networking

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CoBiAt: A Sentiment Classification Model Using Hybrid ConvNet-Dual-LSTM with Attention Techniques

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Informatica (Slovenia) 2023年第4期47卷 523-536页

作者： Ranjan, Roop Daniel, A.K. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology UP Gorakhpur India

Many researchers have recently turned their attention to emotion analysis as a resultant to the number of social reviews of various services. User behaviour may be better understood with the plethora of data, which makes it possible to work toward enhancing QoS. The critical areas of research in language processing are text categorization, which places unorganised data into relevant categories. In several natural language processing (NLP) applications, LSTM and CNN are utilised for text classification. CNN models use techniques to obtain top-level features. In this study, an attention-based model using Dual-LSTM and ConvNet has been proposed. For effectiveness verification of the model, it is trained using two unique datasets. The proposed hybrid model has demonstrated a significant performance gain when compared to previous deep learning techniques. In comparison to other traditional machine learning models, the suggested approach yields outcomes with a higher level of accuracy. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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Fisher's Linear Discriminant Analysis With Space-Folding Operations

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第7期45卷 9233-9240页

作者： Chang, Chin-Chun National Taiwan Ocean University Department of Computer Science and Engineering Keelung City20224 Taiwan

Fisher's linear discriminant analysis (LDA) is an easy-to-use supervised dimensionality reduction method. However, LDA may be ineffective against complicated class distributions. It is well-known that deep feedforward neural networks with rectified linear units as activation functions can map many input neighborhoods to similar outputs by a succession of space-folding operations. This short paper shows that the space-folding operation can reveal to LDA classification information in the subspace where LDA cannot find any. A composition of LDA with the space-folding operation can find classification information more than LDA can do. End-to-end fine-tuning can improve that composition further. Experimental results on artificial and open data sets have shown the feasibility of the proposed approach. © 1979-2012 IEEE.

关键词： Covariance matrix

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On prevention of attribute disclosure and identity disclosure against insider attack in collaborative social network data publishing

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International Journal of Business Intelligence and Data Mining 2023年第1期23卷 14-49页

作者： Kadhiwala, Bintu Patel, Sankita J. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In a collaborative social network data publishing setup, privacy preservation of individuals is a vital issue. Existing privacy-preserving techniques assume the existence of attackers from external data recipients and hence, are vulnerable to insider attack performed by colluding data providers. Additionally, these techniques protect data against identity disclosure but not against attribute disclosure. To overcome these limitations, in this paper, we address the problem of privacy-preserving data publishing for collaborative social network. Our motive is to prevent both attribute and identity disclosure of collaborative social network data against insider attack. For the purpose, we propose an approach that utilises p-sensitive k-anonymity and m-privacy techniques. Experimental outcomes affirm that our approach preserves privacy with a reasonable increase in information loss and maintains an adequate utility of collaborative social network data. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

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IEEE Access 2025年 13卷 89118-89139页

作者： Melvinlara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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