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Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

Intelligent Automation & Soft Computing 2023年第3期35卷 3065-3079页

作者： P.Anantha Prabha G.Suchitra R.Saravanan Department of Computer Science&Engineering Sri Krishna College of TechnologyCoimbatore641042Tamil NaduIndia Department of Electronics and Communication Engineering Government College of TechnologyCoimbatore641013Tamil NaduIndia Department of Marine Pharmacology Faculty of Allied Health SciencesChettinad Academy of Research and EducationKelambakkamChengalpattu603103Tamil NaduIndia

Cephalopods identiﬁcation is a formidable task that involves hand inspection and close observation by a *** observation and iden-tiﬁcation take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods *** the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod ***,the efﬁciency of the chosen models is determined by evaluating their performance and comparing theﬁ***,the models areﬁne-tuned by adding dense layers and tweaking hyperparameters to improve the classiﬁcation of *** models also employ a well-tuned Rectiﬁed Adam optimizer to increase the accuracy ***,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used inﬁne-tuned models to reduce the training *** framework enables an Internet of Things(IoT)or embedded device to perform the classiﬁcation tasks by embedding a suitable lightweight pre-trained ***ﬁne-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classiﬁcation accuracy of 89.74%,87.12%,and 89.74%,***ﬁndings have indicated that theﬁne-tuned models can classify different kinds of *** results have also demonstrated that there is a signiﬁcant reduction in the training time with RAdamGC.

关键词： Cephalopods transfer learning lightweight models classiﬁcation deep learning ﬁsh IoT

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Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

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computers, Materials & Continua 2022年第4期71卷 855-869页

作者： Visvasam Devadoss Ambeth Kumar Chetan Swarup Indhumathi Murugan Abhishek Kumar Kamred Udham Singh Teekam Singh Ramu Dubey Department of Computer Science&Engineering Panimalar Engineering CollegeAnna UniversityChennai600123India Department of Basic Science College of Science&Theoretical StudiesSaudi Electronic UniversityRiyadh-Male Campus 13316Saudi Arabia Department of Computer Science&IT JAIN(Deemed to be University)Bangalore560069India Department of Computer Science and Information Engineering National Cheng Kung UniversityTainan 701Taiwan Department of Mathematics Graphic Era Hill UniversityDehradun248002India Department of Mathematics J.C.Bose University of Science&TechnologyYMCAFaridabad121006India

Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the *** are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory *** cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human *** present,the highly stressful life with bad lifestyle activities causes heart disease at a very young *** main aim of this research is to predict the premature heart disease based on machine learning *** paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage *** Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical *** experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

关键词： Machine learning support vector machine classification cardiovascular disease

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Predicting Hate Words and Offensive Language: A Machine Learning Approach

Predicting Hate Words and Offensive Language: A Machine Lear...

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International Conference on Inventive Systems and Control (ICISC)

作者： K Sugandhi R. Uday Kumar Reddy K. Ravi Kiran Reddy B. Balla Reddy Department of Computer Science&Engineering Vignan Foundation for Science Technology and Research (VFSTR) Vignan University Guntur Andhra Pradesh India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

The internet has led to a significant increase in the amount of information available, which has resulted in the rise of online communication among users. Unfortunately, this has also resulted in the proliferation of toxic online texts, which can lead to bullying, harassment, and personal attacks. While there have been attempts to develop efficient models for predicting toxic comments online, the field is still in its nascent stage, hence new frameworks and strategies are needed to address this issue. Convolutional Neural Networks (CNNs) are a promising solution for text classification due to recent advancements in hardware and big data management. Additionally, the increasing amount of data available necessitates the development of novel machine learning tools for managing this information. This study compares CNNs with the traditional bag-of-words approach as well as more efficient methods for text classification. The results demonstrate that CNNs outperform the conventional method in classifying harmful comments, indicating the approach’s potential for further research in this field. This work highlights the importance of developing innovative strategies to combat online toxicity and provides insight into how deep learning techniques can be used for text classification.

关键词： Text mining Industries Deep learning Toxicology Text categorization Predictive models Control systems

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A Study on Outlier Detection and Feature engineering Strategies in Machine Learning for Heart Disease Prediction

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computer Systems science & engineering 2024年第5期48卷 1085-1112页

作者： Varada Rajkumar Kukkala Surapaneni Phani Praveen Naga Satya Koti Mani Kumar Tirumanadham Parvathaneni Naga Srinivasu Department of Computer Science&Engineering(AIML) MLR Institute of TechnologyHyderabad500043India Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science&Engineering Sir C.R.Reddy College of EngineeringEluru534001India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular ***,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)*** findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning *** work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization *** studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

关键词： Grey wolf optimization ant colony optimization Z-Score interquartile range(IQR) AdaBoost outlier

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer Systems science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational *** initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation Artificial neural network Isotropic triangular grid generation Machine learning

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Enhancing the Adversarial Transferability with Channel Decomposition

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computer Systems science & engineering 2023年第9期46卷 3075-3085页

作者： Bin Lin Fei Gao Wenli Zeng Jixin Chen Cong Zhang Qinsheng Zhu Yong Zhou Desheng Zheng Qian Qiu Shan Yang Sichuan Normal University Chengdu610066China Jinan Geotechnical Investigation and Surveying Institute Jinan250000China School of Computer Science and Engineering Sichuan University of Science&EngineeringZigong643000China School of Computer Science Southwest Petroleum UniversityChengdu610500China AECC Sichuan Gas Turbine Estab Mianyang621000China School of Physics University of Electronic Science and Technology of ChinaChengdu610056China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China Department of Chemistry Physics and Atmospheric ScienceJackson State UniversityJacksonMSUSA

The current adversarial attacks against deep learning models have achieved incredible success in the white-box ***,they often exhibit weak transferability in the black-box scenario,especially when attacking those with defense *** this work,we propose a new transfer-based blackbox attack called the channel decomposition attack method(CDAM).It can attack multiple black-box models by enhancing the transferability of the adversarial *** the one hand,it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate *** the other hand,it helps to escape from local optima by initializing the data point with random ***,it could combine with other transfer-based attacks *** experiments on the standard ImageNet dataset show that our method could significantly improve the transferability of adversarial *** with the state-of-the-art method,our approach improves the average success rate from 88.2%to 96.6%when attacking three adversarially trained black-box models,demonstrating the remaining shortcomings of existing deep learning models.

关键词： Adversarial attack transferability black-box models deep learning

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Fast Intra Mode Selection in HEVC Using Statistical Model

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computers, Materials & Continua 2022年第2期70卷 3903-3918页

作者： Junaid Tariq Ayman Alfalou Amir Ijaz Hashim Ali Imran Ashraf Hameedur Rahman Ammar Armghan Inzamam Mashood Saad Rehman Department of Computer Science&Engineering HITEC UniversityTaxilaPakistan L@BISEN Equipe LSLYncrea Oust20 rue Cuirasse BretagneBrest29200France Department of IT and CS Pak-Austria FachhochschuleHaripurPakistan Department of Electrical Engineering Jouf UniversitySakakaKSA

Comprehension algorithms like High Efficiency Video Coding(HEVC)facilitates fast and efficient handling of multimedia *** algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing ***,the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia ***,a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the ***,the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost(rate-distortion).Firstly,the proposed work utilizes neighboring blocks to extract available information for the current *** this information is converted to the probability that tells which intra mode might be best in the current *** proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be ***,it is also based on optimal stopping theory(OST).Therefore,the proposed model performs better than many existing OST and classical *** proposed algorithms expedited the encoding process by 30.22%of the HEVC with 1.35%Bjontegaard Delta Bit Rate(BD-BR).

关键词： Statistical HEVC angular mode reduce time

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

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computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： Sarah B.Basahel Saleh Bajaba Mohammad Yamin Sachi Nandan Mohanty E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like AI and *** convergence of AI and IoT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDSHS technique exploits IoT devices for the data collection ***,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS *** experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： Internet of things smart healthcare deep learning team work optimizer capsnet fall detection

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