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ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

computer Modeling in engineering & sciences 2022年第2期130卷 823-839页

作者： Nishant Behar Manish Shrivastava Department of Computer Science&Engineering GGVBilaspur495009India

Breast cancer is considered an immense threat and one of the leading causes of mortality in *** is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology *** depends on the accurate identification of the landmarks in the visual artefacts present in the slide *** are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour ***,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of *** study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology ***50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test *** result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,*** accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data *** precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

关键词： Classification histopathology images convolutional neural network breast cancer breakHis

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Enhanced Triple Layered Approach for Mitigating Security Risks in Cloud

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computers, Materials & Continua 2025年第4期83卷 719-738页

作者： Tajinder Kumar Purushottam Sharma Xiaochun Cheng Sachin Lalar Shubham Kumar Sandhya Bansal Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaur135133HaryanaIndia School of Computer Science&Engineering Galgotias UniversityGreater Noida203201Uttar PradeshIndia Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Presidency School of Computer Science Presidency UniversityBangalore560089India Maharishi Markendeshwar Engineering College Maharishi Markandeshwar(Deemed to be)UniversityMullanaAmbala133203India

With cloud computing,large chunks of data can be handled at a small ***,there are some reservations regarding the security and privacy of cloud data *** solving these issues and enhancing cloud computing security,this research provides a Three-Layered Security Access model(TLSA)aligned to an intrusion detection mechanism,access control mechanism,and data encryption *** TLSA underlines the need for the protection of sensitive *** proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard(AES).For data transfer and storage,this encryption guarantees the data’s authenticity and ***,the solution employs the AES encryption algorithm to secure essential data before storing them in the Cloud to minimize unauthorized ***-based access control(RBAC)implements the second strategic level,which ensures specific personnel access certain data and *** RBAC,each user is allowed a specific role and *** implies that permitted users can access some data stored in the *** layer assists in filtering granular access to data,reducing the risk that undesired data will be discovered during the *** 3 deals with intrusion detection systems(IDS),which detect and quickly deal with malicious actions and intrusion *** proposed TLSA security model of e-commerce includes conventional levels of security,such as encryption and access control,and encloses an insight intrusion detection *** method offers integrated solutions for most typical security issues of cloud computing,including data secrecy,method of access,and *** extensive performance test was carried out to confirm the efficiency of the proposed three-tier security *** have been made with state-of-art techniques,including DES,RSA,and DUAL-RSA,keeping into account Accuracy,QILV,F-Measure,Sensitivity,MSE,PSNR,SSIM,and computation time,encryption time,and decryption *** proposed

关键词： Cloud security:data encryption AES access control intrusion detection systems(IDS) role-based access control(RBAC)

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Human-computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

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computer Modeling in engineering & sciences 2023年第5期135卷 1165-1185页

作者： Saiyed Umer Ranjeet Kumar Rout Shailendra Tiwari Ahmad Ali AlZubi Jazem Mutared Alanazi Kulakov Yurii Department of Computer Science&Engineering Aliah UniversityKolkata700156India Department of Computer Science and Engineering National Institute of TechnologySrinagarJammu and Kashmir190006India Department of Computer Science&Engineering Thapar UniversityPatiala147004India Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Engineering National Technical University of UkraineIgor Sikorsky Kyiv Polytechnic InstituteKyiv03056Ukraine

A deep fusion model is proposed for facial expression-based human-computer Interaction ***,image preprocessing,i.e.,the extraction of the facial region from the input image is ***,the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial *** prevent overfitting,in-depth features of facial images are extracted and assigned to the proposed convolutional neural network(CNN)*** CNN models are then ***,the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions,i.e.,fear,disgust,anger,surprise,sadness,happiness,*** experimental purposes,three benchmark datasets,i.e.,SFEW,CK+,and KDEF are *** performance of the proposed systemis compared with some state-of-the-artmethods concerning each *** performance analysis reveals that the proposed system outperforms the competitive methods in terms of various performance ***,the proposed deep fusion model is being utilized to control a music player using the recognized emotions of the users.

关键词： Deep learning facial expression emotions recognition CNN

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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computer Systems science & engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

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Intelligent Automation & Soft Computing 2023年第4期36卷 327-337页

作者： Nandhi Kesavan Latha Department of Computer Science&Engineering Anna University(BIT Campus)Tiruchirappalli620024India

Climate change is the most serious causes and has a direct impact on *** to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to *** to a lack of time frame in existing works,conser-vation adoption affects the performance of existing *** proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its *** Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for ***,the frame-work demonstrates the huge difference in the estimated signiﬁcance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate *** proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

关键词： Machine learning climate change decision support system multiple regression conservation area receiver operating curve

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Web Page Recommendation Using Distributional Recurrent Neural Network

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computer Systems science & engineering 2023年第4期45卷 803-817页

作者： Chaithra G.M.Lingaraju S.Jagannatha Department of Computer Science&Engineering Sapthagiri College of EngineeringBangaloreIndia Department of Information Science and Engineering Sahyadri College of Engineering&ManagementMangaluruIndia Department of Computer Applications MSRITBangaloreIndia

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the *** that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching ***,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation *** improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data *** this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire *** overall work was implemented for the application of the data recommendation *** are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching ***,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query *** was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature *** training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the *** are formed as clusters and paged with proper indexing based on the MPS parameter of similarity *** overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision

关键词： Ontology data mining in big data logarithmic directionality texture pattern metaheuristic pattern searching system distributional recurrent neural network query recommendation

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Multi-dimensional information-driven many-objective software remodularization approach

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Frontiers of computer science 2023年第3期17卷 45-62页

作者： Amarjeet PRAJAPATI Anshu PARASHAR Amit RATHEE Department of Computer Science Engineering&Information Technology Jaypee Istitute of Information TechnologyNoida 201307India Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPunjab 147004India Department of Computer Science Government College BarotaHaryana 131301India

Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.

关键词： software restructuring remodularization multiobjective optimization software coupling and cohesion

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Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

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Frontiers of Structural and Civil engineering 2024年第7期18卷 1084-1102页

作者： Taimur RAHMAN Pengfei ZHENG Shamima SULTANA Department of Civil Engineering World University of BangladeshDhaka 1230Bangladesh School of Civil Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computer Science&Engineering University of Asia PacificDhaka 1205Bangladesh

The precise prediction of the fundamental vibrational period for reinforced concrete(RC)buildings with infilled walls is essential for structural design,especially earthquake-resistant *** learning models from previous studies,while boasting commendable accuracy in predicting the fundamental period,exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models,especially when engaging with continually evolving data *** predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data *** latter should include consistent re-training ability as demanded by realtime,continuously updated data *** research implements an optimized Light Gradient Boosting Machine(LightGBM)model,highlighting its augmented predictive capabilities,realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set,and illuminating its adaptability and efficiency in predictive *** results show that the R^(2) score of LightGBM model is 0.9995 and RMSE is 0.0178,while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient ***,this study introduces a practical application through a streamlit-powered,web-based dashboard,enabling engineers to effortlessly utilize and augment the model,contributing data and ensuring precise fundamental period predictions,effectively bridging scholarly research and practical applications.

关键词： masonry-infilled RC frame fundamental period LightGBM FP4026 research dataset machine learning data-driven approach Bayesian Optimization

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Development of Algorithm for Person Re-Identification Using Extended Openface Method

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computer Systems science & engineering 2023年第1期44卷 545-561页

作者： S.Michael Dinesh A.R.Kavitha Anna University Chennai600025India Department of Computer Science&Engineering SRM Institute of Science&TechnologyChennai600026India

Deep learning has risen in popularity as a face recognition technology in recent ***,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per *** also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)***-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the *** research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face *** proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye *** extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face *** system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.

关键词： Constrained local model enhanced constrained local neuralfield enhanced hog scattered gated expert network support vector machine

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