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Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

Intelligent Automation & Soft Computing 2023年第4期36卷 1137-1149页

作者： P.Saranya P.Asha Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like *** is a complex disease with many subtypes that affect human health without premature treatment and cause *** the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer *** research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature *** paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classiﬁcation(SMF2NC)to solve the above *** predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the *** redundant features are processed marginal weight rates for observing similar features’variants and the absolute *** neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward ***,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classiﬁer produces higher classiﬁcation accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

关键词： Cancer detection extensive data analysis candidate feature selection deep neural classiﬁcation clustering disease inﬂuence rate

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KidneyMultiNet: A Web-Based Automatic System for Kidney Disease Detection Using Hybrid Machine Learning Model From CT Scan Images

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Biomedical Materials and Devices 2024年 1-12页

作者： Rana, Sahab Uddin Nur-A-Alam, Md. Akter, Sadeka Likhon, Md. Nur Hosain Department of Computer Science and Engineering Dhaka University of Engineering & amp Technology Gazipur Dhaka Bangladesh Department of Computer Science and Engineering Sunamgonj Science and Technology University Sylhet Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

Chronic renal disease is the term used to describe kidney function that gradually declines. The kidneys’ final byproduct of eliminating waste and surplus fluid from the bloodstream is urine. Abnormal accumulations of fluid, electrolytes, and waste products can occur in the body when chronic renal disease progresses to a critical degree. Nonetheless, kidney disease has been predicted using various artificial machine learning algorithm approaches. In this study, authors present the “KidneyMultiNet” framework, which combines two models of convolutional neural networks Densenet201 and Xception, which are based on the idea of transfer learning and can predict kidney disease based on kidney CT scan datasets. The “KidneyMultiNet” design that has been suggested makes a thorough and reasonable diagnosis of kidney disease in the kidney conceivable. The authors used a publicly available kidney CT scan image dataset to train and test the proposed model. To determine the efficacy of the output from suggested advanced machine learning CNN methods, the author fed them picture data using a train generator. Then, the author used a rate of learning to decrease the datasets within the proposed model. After completing various learning procedures with many kidney pictures, the proposed model could accurately predict kidney disease, reaching a maximum performance of 99.92%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural network Image processing Kidney disease Machine learning

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Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

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computer Systems science & engineering 2023年第7期46卷 27-42页

作者： D.Viji S.Revathy Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

Cloud storage is essential for managing user data to store and retrieve from the distributed data *** storage service is distributed as pay a service for accessing the size to collect the *** to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage *** potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data *** creates a complex nature to increase the storage consumption under *** resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud ***,preprocessing is done using the sparse augmentation ***,the preprocessed files are segmented into blocks to make *** block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the *** on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a ***,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match *** implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

关键词： Cloud computing deduplication hash indexing relational content analysis document clustering cloud storage record linkage

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Morlet wavelet transformation based deep similarity structured neural learning for image quality assessment

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Multimedia Tools and Applications 2025年第16期84卷 16789-16807页

作者： Balakrishnan, N. Robinson, Y. Harold Department of MCA Sona College of Technology Salem India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India

Quality assessment is a key problem to be resolved in image processing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the process of image quality assessment was not sufficient. A novel method Morlet Wavelet Transformation Based Deep Similarity Structured Neural Learning (MWT-DSSNL) is proposed to enhance the performance of image quality assessment with minimal peak signal-to-noise ratio. The MWT-DSSNL Method is based on artificial neural networks with representation learning. The MWT-DSSNL Method initially gets the number of images from a given dataset as input. The MWT-DSSNL Method uses multiple layers to extract higher-level features from the input images. The MWT-DSSNL Method is a Feed-Forward network where each layer employs the output from the previous layer as input. The MWT-DSSNL Method is designed based on a biological neural network of the human brain. Contrary to the existing method, the MWT-DSSNL Method uses multiple hidden layers and Morlet wavelet transformation to deeply analyze input images and thereby extract features such as luminance, contrast, and structure with a minimal amount of time consumption. By considering the discovered features, finally MWT-DSSNL Method determines structural similarity and thereby exactly identifies the quality of input images with a lower time. From that, the MWT-DSSNL Method achieves enhanced image quality assessment performance when compared to existing works. The simulation of the MWT-DSSNL Method is conducted on factors such as PSNR, average processing time, quality detection accuracy, and false positive rate with different numbers of input images. The simulation result depicts that the MWT-DSSNL method increases the accuracy and also minimizes the time of image quality assessment when compared to conventional works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024

关键词： Network layers

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Energy Efficient Unequal Fault Tolerance Clustering Approach

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computer Systems science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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