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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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Residual multimodal Transformer for expression-EEG fusion continuous emotion recognition

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1290-1304页

作者： Xiaofang Jin Jieyu Xiao Libiao Jin Xinruo Zhang College of Information and Communication Engineering Communication University of ChinaBeijingChina School of Computer Science and Electronic Engineering University of EssexColchesterUK

Continuous emotion recognition is to predict emotion states through affective information and more focus on the continuous variation of emotion. Fusion of electroencephalography (EEG) and facial expressions videos has been used in this field, while there are with some limitations in current researches, such as hand-engineered features, simple approaches to integration. Hence, a new continuous emotion recognition model is proposed based on the fusion of EEG and facial expressions videos named residual multimodal Transformer (RMMT). Firstly, the Resnet50 and temporal convolutional network (TCN) are utilised to extract spatiotemporal features from videos, and the TCN is also applied to process the computed EEG frequency power to acquire spatiotemporal features of EEG. Then, a multimodal Transformer is used to fuse the spatiotemporal features from the two modalities. Furthermore, a residual connection is introduced to fuse shallow features with deep features which is verified to be effective for continuous emotion recognition through experiments. Inspired by knowledge distillation, the authors incorporate feature-level loss into the loss function to further enhance the network performance. Experimental results show that the RMMT reaches a superior performance over other methods for the MAHNOB-HCI dataset. Ablation studies on the residual connection and loss function in the RMMT demonstrate that both of them is functional.

关键词： facial expression recognition human-machine interaction information fusion physiology regression analysis

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Tracing Information Flow in Clustered Complex Networks: Unraveling Centrality Patterns with m-PageRank

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SN computer science 2025年第3期6卷 1-11页

作者： Cheriyan, Jo Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam 686532 India

Social network analysis (SNA) examines the social structures and relational patterns among entities, which are represented as nodes and edges within a network. It finds extensive application in various fields to derive insight into how these entities interact and exert influence on one another. Clustering refers to the tendency of nodes to form cohesive groups, whereby nodes within the same group exhibit higher levels of interconnection compared to those outside it. Understanding the flow of information through these clusters requires advanced methodologies to account for the intricacies and dynamics inherent in clusters, as their complexity often poses significant challenges. This research addresses the complexities associated with the exchange of information within clusters of real-world networks by analyzing the interactions among individual nodes and the pathways along which information is disseminated within these clusters. The article introduces the novel Awareness-Regulated-Spreading (ARS) model, an epidemic-based framework that elucidates how information propagates across networks by considering varying levels of transmissibility, ranging from localized sources to widespread dissemination within complex networks through activation probability. The experimental analysis reveals that the diffusion rate through a single edge is superior to that through triangular edges, and the activation probability surpasses that of state-of-the-art models such as the LACS and GACS models. As the probability of activation of clusters increases, the diffusion of information within these clusters also intensifies, although the clustering coefficient negatively impacts the diffusion process. In addition, the article explores the dynamics of information diffusion in the context of feedback loops and various edge characteristics. These advanced techniques offer deep insights into the flow of information, thereby facilitating more informed decision making in a highly connected worl

关键词： Activation probability Centrality Clusters Complex networks Information diffusion

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2024年第17期84卷 1-21页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Intelligent Vehicular Communication Using Vulnerability Scoring Based Routing Protocol

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Intelligent Automation & Soft Computing 2023年第1期35卷 31-45页

作者： M.Ramya Devi I.Jasmine Selvakumari Jeya Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India

Internet of Vehicles(IoV)is an intelligent vehicular technology that allows vehicles to communicate with each other via *** and the Internet of Things(IoT)enable cutting-edge technologies including such self-driving *** the existing systems,there is a maximum communication delay while transmitting the *** proposed system uses hybrid Cooperative,Vehicular Communication Management Framework called CAMINO(CA).Further it uses,energy efficient fast message routing protocol with Common Vulnerability Scoring System(CVSS)methodology for improving the communication delay,*** improves security while transmitting the messages through *** this research,we present a unique intelligent vehicular infrastructure communication management *** framework includes additional stability for both short and long-range mobile *** also includes built-in cooperative intelligent transport system(C-ITS)capabilities for experimental verification in real-world *** addition,an energy efficient-fast message distribution routing protocol(EE-FMDRP)has been *** combines the benefits between both temporal and direction oriented routing *** has been suggested for distributing information from the origin ends to the predetermined objective in a quick,accurate,and effective manner in the event of an *** critical value scale score(CVSS)employ ratings to measure the assault probability in Markov *** of chained transitions allow us to statistically evaluate the integrity of a group of *** the proposed method helps to enhance the vehicular *** CAMINO with energy efficient fast protocol using CVSS(CA-EEFP-CVSS)method outperforms in terms of shortest transmission latency achieves 2.6 sec,highest throughput 11.6%,and lowest energy usage 17%and PDR 95.78%.

关键词： Intelligent automation intelligent transport system vehicular networks markov chains internet of vehicles critical value scale score

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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D Transformers

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Big Data Mining and Analytics 2024年第2期7卷 485-499页

作者： Sohail Khan Mohammad Nauman Computer Science Department Effat College of EngineeringEffat UniversityJeddah 23341Kingdom of Saudi Arabia

Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on *** of the major challenges in tackling this problem is the complexity of malware analysis,which requires expertise from human *** developments in machine learning have led to the creation of deep models for malware ***,these models often lack transparency,making it difficult to understand the reasoning behind the model’s decisions,otherwise known as the black-box *** address these limitations,this paper presents a novel model for malware detection,utilizing vision transformers to analyze the Operation Code(OpCode)sequences of more than 350000 Windows portable executable malware samples from real-world *** model achieves a high accuracy of 0.9864,not only surpassing the previous results but also providing valuable insights into the reasoning behind the *** model is able to pinpoint specific instructions that lead to malicious behavior in malware samples,aiding human experts in their analysis and driving further advancements in the *** report our findings and show how causality can be established between malicious code and actual classification by a deep learning model,thus opening up this black-box problem for deeper analysis.

关键词： machine learning malware vision transformers Windows Protable Executable(PE)

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Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1863-1880页

作者： S.Amutha J.Raja Sekar Department of Computer Science and Engineering Mepco Schlenk Engineering College(Autonmous)SivakasiIndia

In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each *** Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent *** becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterﬂy Optimization Algorithm(DNN–BBoA)is *** BBoA is incorporated to select the best *** optimal features are fed to the deep neural network classiﬁer and it improves prediction accuracy and reduces the time *** usage of a deep neural network further helps to improve the prediction accu-racy with minimal *** proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle *** proposed work is compared with different machine learning classiﬁers such as Support Vector Machine,Random Forest,and Decision Tree Classiﬁ*** accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.

关键词： Deep neural network cardio vascular disease binarized butterﬂy optimization algorithm feature selection

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Design of Six Element MIMO Antenna with Enhanced Gain for 28/38 GHz mm-Wave 5G Wireless Application

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computer Systems science & engineering 2023年第8期46卷 1689-1705页

作者： K.Jayanthi A.M.Kalpana Department of Electronics and Communication Engineering Government College of EngineeringSalemIndia Department of Computer Science and Engineering Government College of EngineeringSalemIndia

The fifth-generation(5G)wireless technology is the most recent standardization in communication services of interest across the *** concept of Multiple-Input-Multiple-Output antenna(MIMO)systems has recently been incorporated to operate at higher frequencies without *** paper addresses,design of a high-gain MIMO antenna that offers a bandwidth of 400 MHz and 2.58 GHz by resonating at 28 and 38 GHz,respectively for 5G millimeter(mm)-wave *** proposed design is developed on a RT Duroid 5880 substrate with a single elemental dimension of 9.53×7.85×0.8 mm^(3).The patch antenna is fully grounded and is fed with a 50-ohm stepped impedance microstrip *** also has an I-shaped slot and two electromagnetically coupled parasitic slotted *** design is initially constructed as a single-element structure and proceeded to a six-element MIMO antenna configuration with overall dimensions of 50×35×0.8 mm^(3).The simulated prototype is fabricated and measured for analyzing its performance characteristics,along with MIMO antenna diversity performance factors making the proposed antenna suitable for 5G mm-wave and 5G-operated handheld devices.

关键词： MIMO antenna parasitic elements 5G mm-wave dual band frequency diversity factors

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