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Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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Classification of Social Anxiety Disorder Using Explainable Machine Learning and Pearson's Correlation Technique 8

Classification of Social Anxiety Disorder Using Explainable ...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Bathula, Srivarsha Chadaga, Krishnaraj Prabhu, Srikanth Sampathila, Niranjana Manipal Institute of Technology Manipal Academy of Higher Education Manipal Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350350593

Social Anxiety Disorder (SAD) is a widespread mental health issue marked by significant fear or discomfort during social interactions. It can greatly affect a person's life and overall happiness, causing problems like emotional distress, low self-esteem and depression. Thorough examination by a mental health expert is often required to diagnose SAD. The diagnostic and statistical manual of mental disorders' particular criteria are used to diagnose SAD that includes clinical interviews, self-report questionnaires, and a thorough evaluation of the individual's symptoms like severe anxiety while engaging or conversing with strangers. In this work, we employ explainable artificial intelligence (XAI) and machine learning techniques to identify SAD in individuals. Critical attributes were identified using Pearson's correlation technique. Random Forest yields optimal outcomes with 88% accuracy, 93 % precision, 83 % recall, 84 % F1-score, and 94% Area Under Curve (AUC). Furthermore, XAI methods such as Shapley Additive Values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) have been applied to improve the models' accuracy, comprehensibility, and precision. Automated SAD diagnosis helps in early detection and increased accessibility that allows for timely intervention, treatment, and facilites access to social anxiety testing and screening for an individual. © 2024 IEEE.

关键词： Adversarial machine learning

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CodGRU: Covariate gated attention GRU for short term runoff forecasting 24

CodGRU: Covariate gated attention GRU for short term runoff ...

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4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Cao, Jiahao Xiao, Tong Cao, Zhe School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China Shenzhen Branch of China Academy of Urban Planning and Design Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. The generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. The test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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An Improved Deadlock Recovery Policy of Flexible Manufacturing Systems Based on Resource Flow Graphs

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IEEE Access 2024年 12卷 65202-65212页

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan R.O.C. Air Force Academy Mathematics and Physics Division General Education Center Gangshan Kaohsiung820009 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung807618 Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung413310 Taiwan

The system deadlock problem of the flexible manufacturing system (FMS) needs to be solved urgently. Deadlocks may occur when resources are repeatedly requested by multiple working processes, where such competition causes a lack of resources, and the system cannot get into subsequent processes smoothly. In this paper, we build FMS by using a particular class of Petri Net (PN) model called a system of simple sequential processes with resources (S3PR) net, which features a high utilization rate of resources. When parts of resources are held by related processes and these processes wait for others to release resources to move on, a circular waiting situation occurs. Circular waiting is one of the conditions for system deadlocks occurring, where the system under this situation has a high probability of causing system deadlocks. The resources being held have to be released to break the waiting states. This paper develops a novel control transition-based deadlock recovery policy by combining graph theory and reachability graph analysis technique. The proposed approach firstly builds a subnet of the PN model called resource flow graph (RFG) and finds out all its hold and request circuits (HRCs), which may lead to a circular waiting situation. In addition, we design a new algorithm to obtain the minimal set of control transitions further. We introduce three PN models of FMS to demonstrate the proposed method. Through experimental results and comparison between related works, it is known that the proposed deadlock recovery uses fewer controllers and has the same performance. © 2013 IEEE.

关键词： Frequency modulation

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Unidirectional Negative Refraction at an Exceptional Point of Acoustic P T-Symmetric Systems

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Chinese Physics Letters 2023年第12期40卷 56-61页

作者：刘宸蓝君顾仲明祝捷 Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China State Key Laboratory of Acoustics Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology Nanjing Tech UniversityNanjing 211800China

We demonstrate a method to realize unidirectional negative refraction in an acoustic parity-time(P T)-symmetric system, which is composed of a pair of metasurfaces sandwiching an air gap. The pair of metasurfaces possesses loss and gain modulations. The unidirectional negative refraction, which is strictly limited to the case of incident wave imposing on the loss end of the metasurface, is demonstrated at the exception point(EP) in this P T-symmetric system, while the incidence from the other side leads to strong reflection. Based on rigorous calculations, we explicitly show the underlying mechanism of this model to achieve unidirectional wave scatterings around the EP in the parametric space. In addition, the perfect imaging of a point source in the three-dimensional space, as a signature of negative refraction, is simulated to provide a verification of our work. We envision that this work may sharpen the understanding of P T-symmetric structures and inspire more acoustic functional devices.

关键词： refraction unidirectional symmetric

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Skin Cancer Classification Using Multi-Classification Deep Learning Model Based on Dermoscopic Images

Skin Cancer Classification Using Multi-Classification Deep L...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Devi, K. Guru Joshna Vamsi Krishna, B. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350359688

On the list of the deadliest diseases affecting humans, skin cancer ranks high. The current health care system makes skin cancer detection a tedious process, which can be life-threatening if not caught early enough. The best chance of a full recovery from skin cancer is achieved by early detection. The use of DL algorithms for skin cancer detection has been increasingly common in recent years. The goal of this work was to develop a multi-classification method for skin cancer diagnosis using a DL model. In this study, we present a new model, DSCC Net, a convolutional-neural-network (CNN) based deep-learning (DL) skin cancer classification network and test it on three open-source standard datasets: ISIC 2020, HAM10000, DermIS. In classifying the different kinds of skin cancer, the Proposed DSCC Net achieved an AUC of 99.43%, an accuracy of 94.12%, a recall of 95%, a precision of 94%, and an F1-score of 94%. Our Proposed DSCC-Net model outperformed baseline models, providing dermatologists and other medical professionals with substantial assistance in the analysis of skin cancer. © 2024 IEEE.

关键词： Lung cancer

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Malware Detection Employing Deep Neural Networks 10

Malware Detection Employing Deep Neural Networks

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Nayak, Sanjana Ganesh Kurup, Samir Andrew, J. Manipal Institue of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350384369

Malware, malicious software designed to disrupt, damage, or gain unauthorized access to computer systems, poses a significant and evolving threat to cybersecurity. Malware detection is an essential component of modern cybersecurity, given the escalating complexity and diversity of malicious software threats. In this study, we present a novel approach to malware detection based on behavior-based datasets using a fully connected deep neural network. Our research is motivated by the need for robust and accurate malware detection models that can adapt to evolving threats. The behavior-based dataset, which captures the dynamic interactions of malware with the host environment, provides a rich source of information for training and evaluation. The model uses the hyperbolic tangent (tanh) activation function and the Nesterov optimizer, resulting in remarkable accuracy of 100%. This study offers a high- performing solution for malware detection using behavior- based datasets. As cybersecurity continues to evolve, our approach contributes to strengthening defenses against the ever- persistent threat of malware. © 2024 IEEE.

关键词： Cyber attacks

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Advancing Farming Practices: Machine Learning Algorithms for Optimal Crop Selection 2

Advancing Farming Practices: Machine Learning Algorithms for...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Bhatnagar, Shaleen Chadalawada, Archit Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383652

This study underscores the critical importance of the agricultural sector for ensuring global food security and economic resilience, emphasizing the role of machine learning (ML)-enhanced crop recommendation systems. These systems are increasingly crucial for guiding farmers in choosing crops that are best suited to their unique soil and environmental conditions. Through the analysis of a comprehensive dataset, including soil composition, climate variations, historical crop yields, and agricultural practices, this research evaluates the performance of several ML algorithms-such as neural networks, decision trees, random forests, and support vector machines-in forecasting the most appropriate crops. Training on historical data allows these algorithms to decipher the complex dynamics between environmental factors and crop results. The success of these models is gauged by metrics including accuracy, precision, recall, and F1-score. This study also delves into the models' interpretability, offering crucial insights to both agricultural practitioners and researchers. Moreover, it presents the creation of a user-friendly web application that applies these ML models to provide personalized crop recommendations, requiring user inputs like geographical location, soil characteristics, and weather data. The findings reveal that ML algorithms can significantly empower farmers with knowledge to select suitable crops, thereby boosting agricultural efficiency, optimizing the use of resources, and enhancing the sustainability of agricultural operations. The research highlights the necessity of selecting the right ML algorithms and preprocessing techniques for achieving superior results. Notably, we have enhanced the accuracy of the random forest algorithm to 99.92% and ADABOOST with the Decision Tree Classifier to 99.02%. This contribution to precision agriculture showcases the practical use of ML in crop recommendation systems as a technological approach to addressing agricultural

关键词： Adversarial machine learning

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