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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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AdaFedProx: A Heterogeneity-Aware Federated Deep Reinforcement Learning for Medical Image Classification

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IEEE Transactions on Consumer electronics 2024年第1期71卷 1432-1441页

作者： Sahoo, Pranab Tripathi, Ashutosh Saha, Sriparna Mondal, Samrat Singh, Jyoti Prakash Sharma, Bhisham Computer Science and Engineering Department Indian Institute of Technology Patna India Electrical and Electronics Engineering Department The Rajiv Gandhi Institute of Petroleum Technology India Indian Institute of Technology Computer Science and Engineering Department Patna India National Institute of Technology Computer Science and Engineering Department Patna India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India

In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in data sharing due to privacy concerns. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without direct access to patient data. This decentralized approach utilizes data from diverse sources, resulting in a globally learned model with satisfactory performance across individual sites. However, federated training encounters challenges such as "system heterogeneity"in client specifications and "data heterogeneity"in imbalanced data distribution. Standard FL methods become unstable and require extensive hyperparameter tuning for optimal performance when facing heterogeneous clients, posing challenges in real-world applications. This research introduces a novel heterogeneity-aware federated learning approach named AdaFedProx to address the performance degradation resulting from heterogeneity. AdaFedProx utilizes a reinforcement learning-driven strategy, introducing a tailored proximal term into the objective function to incorporate partial client information in heterogeneous settings. Dynamically determining the proximal term based on individual client states, including critical characteristics like data distribution, system specification, and performance feedback, empowers AdaFedProx to make regularized decisions throughout the federated training. AdaFedProx consistently outperforms four state-of-the-art methods on three medical datasets across diverse heterogeneity settings (0%, 50%, and 75% straggler). The code can be found at https://***/Pranabiitp/AdaFedProx. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Secure approach to sharing digitized medical data in a cloud environment

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Data science and Management 2024年第2期7卷 108-118页

作者： Kukatlapalli Pradeep Kumar Boppuru Rudra Prathap Michael Moses Thiruthuvanathan Hari Murthy Vinay Jha Pillai Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Electronics and Communication Engineering CHRIST(Deemed to be University)Bengaluru560074India

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of s

关键词： Electronic medical records Cloud computing Data privacy Attribute-based encryption Authentication

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

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computer Systems science & engineering 2023年第2期44卷 1719-1733页

作者： S.Nandagopal G.Karthy A.Sheryl Oliver M.Subha Department of Computer Science and Engineering Nandha College of TechnologyErode638052TamilnaduIndia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science and Engineering St.Joseph's College of EngineeringChennai600119TamilnaduIndia Department of Electronics and Communication Engineering University College of Engineering NagercoilNagercoil629004TamilnaduIndia

Human Action Recognition(HAR)and pose estimation from videos have gained signiﬁcant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,*** considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difﬁcult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so *** the literature,it is observed that hard to deal with the temporal dimension in the action recognition *** neural network(CNN)models could be used widely to solve *** this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity *** KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose *** the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human ***,an optimal DCNN model is developed to classify the human activities label based on the extracted key *** improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch *** experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

关键词： Human activity recognition pose estimation key point extraction classiﬁcation deep learning RMSProp

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

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Tsinghua science and Technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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RENOWNED: A Real-Time Anomaly Detection and Mitigation Framework in Edge-Enabled IoV

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IEEE Internet of Things Journal 2025年第12期12卷 20695-20706页

作者： Pal, Chandrajit Saha, Sangeet Zhai, Xiaojun McDonald-Maier, Klaus University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom

The rapid adoption of smart vehicles and their interconnection through the Internet of Vehicles (IoV) has increased the use of electronic control units (ECUs) in cars. These ECUs, while enabling advanced features, also present a larger target for cyberattacks, which can disrupt critical functions and jeopardize safety. The time-sensitive nature of automotive systems necessitates swift responses, making the protection of ECUs crucial. The imprecise computation (IC) task model can mitigate the risk of task completion failures by generating acceptable approximation results within deadlines when achieving absolute accuracy becomes difficult within fixed deadlines and energy budgets. This article introduces RENOWNED, a solution that ensures the normal functioning of these controller area networks (CAN) controlled ECUs even in the face of anomalies. It combines anomaly detection and mitigation through the HEALING module to maintain the desired performance. The anomaly detection module uses graph attention networks (GAT) to identify unusual processor behavior. If an anomaly is detected, the HEALING module takes over, reallocating tasks based on the available resources to guarantee that deadlines are met and energy constraints are not exceeded. Experiments have shown that RENOWNED delivers a Quality of Service (QoS) of 25% to 64% when system utilisation is varied in the range from 40% to 90%. It exhibits an excelling performance in detecting anomalies, achieving a 97.6% accuracy even when the magnitude mixed anomaly signals are very minute. Thus, our proposed RENOWNED offers a robust way to enhance the reliability and energy efficiency of safety-critical automotive applications prevalent in IoV. © 2014 IEEE.

关键词： Budget control

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