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| 1 | An Optimal Classification Model for Rice Plant Disease Detection显示文摘Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942. | R.Sowmyalakshmi T.Jayasankar V.Ayyem PiIllai Kamalraj Subramaniyan Irina V.Pustokhina Denis A.Pustokhin K.Shankar | 2021 | Computers, Materials & Continua2021，，8： | 1 |
| 2 | A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification显示文摘Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN. | D.Venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly Irina V.Pustokhina Denis A.Pustokhin K.Shankar | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 3 | Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm显示文摘Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind. | J.Jayanthi M.Kavitha T.Jayasankar A.Sagai Francis Britto N.B.Prakash Mohamed Yacin Sikkandar C.Bharathiraja | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 4 | Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems显示文摘Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures. | P.Ramadevi T.Jayasankar V.Dinesh M.Dhamodaran | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 5 | Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function显示文摘The human face forms a canvas wherein various non-verbal expressions are communicated.These expressional cues and verbal communication represent the accurate perception of the actual intent.In many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person experiences.Even when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro expressions.These micro expressions cannot be masked and reflect the actual emotional state of a person under study.Suchmicro expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize them.This necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on display.The study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to identify.This research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less accuracy.One of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the outcomes.The proposed swarm-based technique handles this in the research to precisely align and crop the subsequent frames,resulting in much superior detection rates in identifying the micro expressions when on display. | A.N.Arun P.Maheswaravenkatesh T.Jayasankar | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 6 | Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments显示文摘The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any business.It has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the data.With the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important issue.This paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in CCEs.The complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex CCEs.An access control Encryption Method(EM)is used to improve the encryption.The Complexity is measured to increase the authentication standard.The active attack is protected using this ISIMSDS methodology.Our proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing rapidly.The security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related methods.The performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data. | Saraswathi Shunmuganathan Sridharan Kannan T.V.Madhusudhana Rao K.Ambika T.Jayasankar | 2022 | Computer Systems Science & Engineering2022，43，11： | 0 |
| 7 | Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment显示文摘Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established. | J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar | 2022 | Computers, Materials & Continua2022，，1： | 0 |
| 8 | Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment显示文摘Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy. | C.Rajinikanth P.Selvaraj Mohamed Yacin Sikkandar T.Jayasankar Seifedine Kadry Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 9 | An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm显示文摘In the recent years,microarray technology gained attention for concurrent monitoring of numerous microarray images.It remains a major challenge to process,store and transmit such huge volumes of microarray images.So,image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily.Various techniques have been proposed in the past with applications in different domains.The current research paper presents a novel image compression technique i.e.,optimized Linde–Buzo–Gray(OLBG)with Lempel Ziv Markov Algorithm(LZMA)coding technique called OLBG-LZMA for compressing microarray images without any loss of quality.LBG model is generally used in designing a local optimal codebook for image compression.Codebook construction is treated as an optimizationissue and can be resolved with the help of Grey Wolf Optimization(GWO)algorithm.Once the codebook is constructed by LBGGWO algorithm,LZMA is employed for the compression of index table and raise its compression efficiency additionally.Experiments were performed on high resolution Tissue Microarray(TMA)image dataset of 50 prostate tissue samples collected from prostate cancer patients.The compression performance of the proposed coding esd compared with recently proposed techniques.The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques. | R.Sowmyalakshmi Mohamed Ibrahim Waly Mohamed Yacin Sikkandar T.Jayasankar Sayed Sayeed Ahmad Rashmi Rani Suresh Chavhan | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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