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In recent days, the incidence of Diabetic Retinopathy (DR)has become high, affecting the eyes because of drastic increase in the glucose level in blood. Globally, almost half of the people under the age of 70 gets severely affected by diabetes. In the absence of earlier recognition and proper medication, the DR patients tend to lose their vision. When the warning signs are tracked down, the severity level of the disease has to be validated so to take decisions regarding appropriate treatment further. The current research paper focuses on the concept of classification of DR fundus images on the basis of severity level using a deep learning model. This paper proposes a deep learning-based automated detection and classification model for fundus DR images. The proposed method involves various processes namely preprocessing, segmentation and classification. The methods begins with preprocessing stage in which unnecessary noise that exists in the edges is removed. Next, histogram-based segmentation takes place to extract the useful regions from the image. Then, Synergic Deep Learning (SDL) model was applied to classify the DR fundus images to various severity levels. The justification for the presented SDL model was carried out on Messidor DR dataset. The experimentation results indicated that the presented SDL model offers better classification over the existing models.

Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Different data classification models were developed to foresee the financial crisis of an organization using their historical data. One important step towards the development of accurate financial crisis prediction (FCP) model involves the selection of appropriate variables (features) which are relevant for the problems at hand. This is termed as feature selection problem which helps to improve the classification performance. This paper proposes an Ant Colony Optimization (ACO) based financial crisis prediction (FCP) model which incorporates two phases: ACO based feature selection (ACO-FS) algorithm and ACO based data classification (ACO-DC) algorithm. The proposed ACO-FCP model is validated using a set of five benchmark dataset includes both qualitative and quantitative. For feature selection design, the developed ACO-FS method is compared with three existing feature selection algorithms namely genetic algorithm (GA), Particle Swarm Optimization (PSO) algorithm and Grey Wolf Optimization (GWO) algorithm. In addition, a comparison of classification results is also made between ACO-DC and state of art methods. Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts. In consequence, this study strongly recommends that the proposed ACO-FCP model is highly competitive than traditional and other artificial intelligence techniques.

Presently, BlockChain (BC) gained significant interest because of its undeniable nature and related advantages of security and privacy, BC has the power to resolve the limitations of Internet of Things (IoT) such as data protection and privacy. At the same time, BC has high computation complexity, restricted scalability, high bandwidth overhead and latency that is unsuitable to IoT. In this paper, efficient Lightweight integrated Blockchain (ELIB) model is developed to meet necessitates of IoT. The presented model is deployed in a smart home environment as an important illustration to verify its applicability in various IoT scenarios. The resource constrained resources in a smart home takes the advantages from a centralized manager which generates shared keys to transmit data, process every incoming and outgoing requests. The presented ELIB model generates an overlay network where highly equipped resources can merges to a public BC which verifies dedicated security and privacy. A set of three optimizations are carried out in the presented ELIB model include lightweight consensus algorithm, certificateless (CC) cryptography and Distributed Throughput Management (DTM) scheme. A detailed simulation takes place under different scenarios in terms of processing time, energy usage and overhead. The ELIB attains a total of 50% saving in processing time on comparing to baseline method with the minimum energy consumption of 0.07mJ. The obtained experimental outcome indicated that the ELIB shows maximum performance under several evaluation parameters.

This paper presents a method of designing independent fractional order PI/PID controller for two interacting conical tank level (TICTL) process based on the Equivalent Transfer Function (ETF) model and simplified decoupler. The TICTL process is decomposed into independent single input single output (SISO) model using ETF. A bat optimization algorithm is utilized to independently design a diagonal fractional order PI/PID controller based on ETF model. The effectiveness of the proposed method is illustrated with simulation examples and also the experimental TICTL process utilized to validate the proposed method.

In present days, prediction of financial crisis of a company is a hot research area. The use of data mining and machine learning algorithms assists to resolve the financial crisis prediction (FCP) problem. Since financial data contain more demographical and unwanted information, it might decrease the classification performance significantly. So, feature selection (FS) process is applied to choose useful data and remove the irrelevant repetitive data. This paper introduces a novel predictive framework for FCP model by the incorporation of improved grey wolf optimization (IGWO) and fuzzy neural classifier (FNC). An IGWO algorithm is derived by the integration of GWO algorithm and tumbling effect. The presented IGWO-based FS method is employed to discover the optimal features from the financial data. For classification purposes, FNC is employed. The proposed method is experimented on two benchmark data sets, namely Australian Credit and German data set under several of performance metrics. The experimental values verified the superior nature of the proposed FCP model over the compared methods.

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

Alzheimer Detection (AD) is one of the most common memory depletion diseases that occurs at an older age and is also, a widely recognized type of dementia. In this paper, we propose a novel model for AD with Brain Image Analysis (BIA). Initially, the image database is considered for the removal of the unwanted region. Then an intermediate output is sent to extract the considerable features such as, texture, histogram, and scale-invariant transform from the magnetic resonance images of the brain. The Group Grey Wolf Optimization (GGWO) technique is used to increase the detection performance using Decision Tree, K-Nearest Neighbor, and Convolutional Neural Network classifiers. These are used to identify the reduced set of useful features without degrading the performance. The proposed approach achieved 96.23% of accuracy for the detection of Alzheimer disease, in comparison to the other competitive schemes in the existing literature.

The ever-growing advancement in communication innovation of modern smart objects carries with it a new era of improvement for the Internet of Things (IoT) based networks. The health care system is the best approach to store the patient’s health data online with high privacy. Ensuring the privacy and confidentiality of patient information in the cloud is of utmost importance; here, the enhanced security model of healthcare data gives rise to trust. For the secure communication, the healthcare data sensed by the IoT sensor network is encrypted by Lightweight SIMON block cipher. For improving the privacy of healthcare data among individuals, we implemented the share generation model. Then, share creation model, i.e., Chinese Remainder Theorem (CRT) is developed to generate the copy of every ciphertext based on the selected number of users and the data is shared among the optimal number of users. The selection of the users in IoHT is made by the metaheuristic algorithm called Hybrid Teaching and Learning Based Optimization (HTLBO). Then, we present healthcare service providers for giving the full scope of medical services to people enrolled in IoHT. The performance of Secure Data is approved through simulations in terms of energy cost, computation time, etc., of the proposed algorithms and the outcomes demonstrate that Secure Data can be efficient while applying for ensuring security chances in IoT-based healthcare systems.

Rapid growth in urban population creates various kinds of issues like long hours traffic-jams, pollution which makes the city life insecure and non-liveable. The concept of a smart city is introduced to improve the quality of city life. Smart cities are being developed to satisfy the need for the safety of its users’ and secure journeys over in the urban scenario by proposing the smart mobility concept. At the same time, Vehicular adhoc network (VANET) comes under the type of mobile adhoc network (MANET), wherever the vehicles are treated as nodes in a network. The application of Big Data technologies to VANET gains useful insight from the massive quantity of operational data to enhance traffic management process like planning, engineering as well as operation. During the real-time processes, the VANET generates large data, and the VANET characteristics are mapped to Big Data attributes. Moreover, ant colony optimization (ACO) algorithm is employed for routing in vehicular networks over Hadoop Map Reduce standalone distributed framework and over multi-node cluster with 2, 3, 4 and 5 nodes. The simulation outcomes ensure that the processing time of the algorithm is significant decreases with a rise in the node count of the Hadoop framework.

Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from a CT lung images and then dimensionality of feature is reduced using LDR to classify lung nodules as either malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%.

The internet of things (IoT) is an internet among things through advanced communication without human’s operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.

Financial crisis prediction (FCP) plays a vital role in the economic phenomenon. The precise prediction of the number and possibility of failing firms acts as an index of the growth and strength of a nation’s economy. Traditionally, several methods have been presented for effective FCP. On the other hand, the classification performance and prediction accuracy and data legality is not good enough for practical applications. In addition, many of the developed methods perform well for some of the particular dataset but not adaptable to different dataset. Hence, there is a requirement to develop an efficient prediction model for better classification performance and adaptable to diverse dataset. This paper presents a cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based classification model. In the first stage, an improved K-means algorithm is devised to eliminate the wrongly clustered data. Then, a rule-based model is selected to design to fit the given dataset. At the end, FSCGACA is employed for seeking the optimal parameters of the rule-based model. The proposed algorithm is employed to a collection of three benchmark dataset which include qualitative bankruptcy dataset, Weislaw dataset and Polish dataset. A detailed statistical analysis of the dataset is also given. The results analysis ensured that the presented FCP model is superior to other classification model based on the different measures and also found to be more appropriate for diverse dataset.

Secret Image Sharing (SIS) scheme is to encrypt a secret image into ‘n’ specious shadows. It is unable to reveal any data on the secret image if at least one of the shadows is not achieved. In this paper, wavelet-based secret image sharing scheme is proposed with encrypted shadow images using optimal Homomorphic Encryption (HE) technique. Initially, Discrete Wavelet Transform (DWT) is applied on the secret image to produce sub bands. From this process, multiple shadows are created, encrypted and decrypted for each shadow. The encrypted shadow can be recovered just by choosing some subset of these ‘n’ shadows that makes transparent and stack over each other. To improve the shadow security, each shadow is encrypted and decrypted using HE technique. For the concern on image quality, the new Oppositional based Harmony Search (OHS) algorithm was utilized to generate the optimal key. From the analysis, it shows that the proposed scheme provide greater security compared to other existing schemes.

In this paper, an Internal model Controller (IMC) based PID with fractional filter for a first order plus time delay process is proposed. The structure of the controller has two parts, one is integer PID controller part cascaded with fractional filter. The proposed controller has two tuning factors λ, filter time constant and a, fractional order of the filter. In this work, the two factors are decided in order to obtain low Integral Time Absolute Error (ITAE). The effectiveness of the proposed controller is studied by considering a non linear (hopper tank) process. The experimental set up is fabricated in the laboratory and then data driven model is developed from the experimental data. The non linear process model is linearised using piecewise linearization and two linear regions are obtained. At each operating point, linear first order plus dead time model is obtained and the controller is designed for the same. To show the practical applicability, the proposed controller is implemented for the proposed experimental laboratory prototype.

The development of the Internet of Things (IoT) is predicted to change the healthcare industry and might lead to the rise of the Internet of Medical Things. The IoT revolution is surpassing the present-day human services with promising mechanical, financial, and social prospects. This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies. For the most part, the patient data are stored as a cloud server in the hospital due to which the security is vital. So another framework is required for the secure transmission and effective storage of medical images interleaved with patient information. For increasing the security level of encryption and decryption process, the optimal key will be chosen using hybrid swarm optimization, i.e., grasshopper optimization and particle swarm optimization in elliptic curve cryptography. In view of this method, the medical images are secured in IoT framework. From this execution, the results are compared and contrasted, whereas a diverse encryption algorithm with its optimization methods from the literature is identified with the most extreme peak signal-to-noise ratio values, i.e., 59.45 dB and structural similarity index as 1.

Sites for web-based shopping are winding up increasingly famous these days. Organizations are anxious to think about their client purchasing conduct to build their item deal. Internet shopping is a method for powerful exchange among cash and merchandise which is finished by end clients without investing a huge energy spam. The goal of this paper is to dissect the high-recommendation web-based business sites with the help of a collection strategy and a swarm-based improvement system. At first, the client surveys of the items from web-based business locales with a few features were gathered and, afterward, a fuzzy c-means (FCM) grouping strategy to group the features for a less demanding procedure was utilized. Also, the novelty of this work—the Dragonfly Algorithm (DA)—recognizes ideal features of the items in sites, and an advanced ideal feature-based positioning procedure will be directed to discover, at long last, which web-based business webpage is best and easy to understand. From the execution, the outcomes demonstrate the greatest exactness rate, that is, 94.56% compared with existing methods.

A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%.

The tedious challenging of Big Data is to store and retrieve of required data from the search engines. Problem Defined There is an obligation for the quick and efficient retrieval of useful information for the many organizations. The elementary idea is to arrange these computing files of organization into individual folders in an hierarchical order of folders. Manually, to order these files into folders, there is an ardent need to know about the file contents and name of the files to give impression of files, so that it provides an alignment of certain set of files as a bunch. Problem Statement Manual grouping of files has its own complications, for example when these files are in numerous amounts and also their contents cannot be illustrious by their labels. Therefore, it’s an intense requirement for Document clustering with data processing machines for enthusiastic results. Existing System A couple of analyzers are impending with dynamic algorithms and comprehensive analogy of extant algorithms, but, yet, these have been restricted to organizations and colleges. After recent updated rules of NMF their raised a self interest in document clustering. These rules gave trust in its performances with better results when compared to Latent Semantic Indexing with Singular Value Decomposition. Proposed System A new working miniature called Novel K-means Non-Negative Matrix Factorization (KNMF) is implemented using renovated guidelines of NMF which has been diagnosed for clustering documents consequently. A new data set called Newsgroup20 is considered for the exploratory purpose. Removal of common clutter/stop words using keywords from Key Phrase Extraction Algorithm and a new proposed Iterated Lovin stemming will be utilized in preprocessing step inassisting to KNMF. Compared to the Porter stemmer and Lovins stemmer algorithms, Iterative Lovins algorithm is providing 5% more reduction. 60% of the document terms are been minimized to root as remaining terms are already root words. Eventually, an appeal to these processes named “Progressive Text mining radical” is developed inlateral exertion of K-Means algorithm from the defined Apache Mahout Project which is used to analyze the performance of the MapReduce framework in Hadoop.

Thyroid diseases are across the board around the world. In India as well, there is a critical issue caused because of this disease. Different research studies estimate that around 42 million individuals in India suffer from the ill effects of “thyroid diseases.” Diagnosis of health situations is an energetic and testing undertaking in the field of therapeutic science. Our proposed model is to classify this thyroid data utilizing optimal feature selection and kernel-based classifier process. We will create classifications models and its group show for classification of data using “multi kernel support vector machine.” The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization. Reason for this optimal feature selection as the insignificant features from unique dataset and computationally increment the performance of the model. The proposed thyroid classification results in the accuracy, sensitivity, and specificity of 97.49, 99.05 and 94.5%, compared to the existing model. This performance measure is computed from the confusion matrix with the diverse measures contrasted with individual models and in addition to the existing classifier and optimization techniques.

Social Internet of Things (SIoT) supports many novel applications and networking services for the IoT in a more powerful and productive way. In this paper, we have introduced a hierarchical framework for feature extraction in SIoT big data using map-reduced framework along with a supervised classifier model. Moreover, a Gabor filter is used to reduce noise and unwanted data from the database, and Hadoop Map Reduce has been used for mapping and reducing big databases, to improve the efficiency of the proposed work. Furthermore, the feature selection has been performed on a filtered data set by using Elephant Herd Optimization. The proposed system architecture has been implemented using Linear Kernel Support Vector Machine-based classifier to classify the data and for predicting the efficiency of the proposed work. From the results, the maximum accuracy, specificity, and sensitivity of our work is 98.2%, 85.88%, and 80%, moreover analyzed time and memory, and these results have been compared with the existing literature.

With the rapid development in the field of medical image encryption, researchers analyzed a number of encryption algorithms based on chaotic systems. But there is a difficulty lies, i.e., small key space and weak security in 1-D chaotic cryptosystems. To overcome this difficulty, an alternative security model was proposed in this paper. This paper investigated highly secure medical images with a couple of subkeys, initially where a couple of subkeys are given by utilizing chaotic logistic and tent maps. As per the chaotic (C-function) process, the security was investigated like diffusion as well as confusion. Based on the initial conditions, different random numbers were generated for each map from chaotic maps. Adaptive grasshopper optimization algorithm with PSNR and correlation coefficient fitness function was proposed to choose the optimal secret and public key of the system among the random numbers. The reason behind choosing adaptive process is to enhance high-security investigation of the current proposed model compared to the existing methods. At last, the proposed strategy results were compared with existing security methods and literary works, but found to be high performing.

An independent design of adaptive multiloop fractional order internal model control based PID (FOIMC-PID) controller for a two input two output process (TITO) is presented. The multivariable system is decomposed by ideal decoupler to develop effective open loop transfer function (EOTF) and then it is approximated into first order plus dead time (FOPDT) model. The multiloop FOIMC-PID controller is designed for TITO model and controller parameters are optimally tuned using Novel Bat Optimization Algorithm (NBOA). The simulation results are demonstrated to show the effectiveness of proposed controller and also some tuning procedure is suggested to get better FOIMC-PID controller performance.

In this work, the control of first order delay integrating process (FODIP) is considered for study. Integrating processes are difficult to control due to the presence of a pole at origin. PD controller could be preferred in such cases for control of the processes. To improve the servo response and disturbance rejection, PD controller alone is not enough, hence Internal Model Control (IMC) structure is used for deriving PD controller parameters. The time delay in the process transfer function is an important factor to be considered when designing a controller and need a proper approximation. First order taylor series and pade approximations are considered for this work. The superiority of fractional theory in control area is also a motivation for this research work to design a non-integer controller. Hence, integer and non integer filter with IMC PD controller is derived using first order taylor series and pade approximations. Simulation study is done using Matlab/Simulink and the results show the improved performance indices of the designed controller.

In recent times, earlier diagnosis of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Since the chest computed tomography (CT) image diagnosis requires medical experts and more time, an automated intelligent model needs to be developed for effective COVID-19 diagnosis. This paper presents a new automated COVID-19 diagnosis model using optimal grey level co-occurrence matrix (GLCM) based feature extraction and Extreme Learning Machine (ELM) based classification. The input chest images undergo pre-processing to improve the image quality. Next, the optimal GLCM features are derived by the use of Elephant Herd Optimisation (EHO) algorithm. Then, the ELM model is applied to perform the classification task. The performance of the OGLCM-ELM model has been validated using the benchmark dataset and the experimental outcome ensured the superior performance of the proposed model over the compared methods. The proposed OGLCM-ELM model has achieved maximum sensitivity of 89.56%, specificity of 90.45%, F-score of 90.13% and accuracy of 90.69%.

Security is the most critical issue amid transmission of medical images because it contains sensitive information of patients. Medical image security is an essential method for secure the sensitive data when computerized images and their relevant patient data are transmitted across public networks. In this paper, the dual encryption procedure is utilized to encrypt the medical images. Initially Blowfish Encryption is considered and then signcryption algorithm is utilized to confirm the encryption model. After that, the Opposition based Flower Pollination (OFP) is utilized to upgrade the private and public keys. The performance of the proposed strategy is evaluated using performance measures such as Peak Signal to Noise Ratio (PSNR), entropy, Mean Square Error (MSE), and Correlation Coefficient (CC).

Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage resulting to increased mortality rate. The OC data generated from the Internet of Medical Things (IoMT) can be used to identify distinguish the OC. To achieve this, we utilize Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN) to classify OC. SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In addition, an optimal classifier named optimal recurrent neural network (ORNN) is also employed. The classification rate of OC detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO) algorithm. A set of experimentation is carried out using the data collected from women who have a high danger of OC because of familial or individual history of cancer. The proposed method attains a maximum accuracy of 96.27 with the sensitivity and specificity rate of 85.2 respectively when compared to recurrent neural networks (RNN), feedforward neural networks (FFNN) and so on. The experimental results verified that the proposed model can be used to detect cancer at early stages with high accuracy, sensitivity, specificity and low root mean square error (RMSE).

The objective of this paper is to design and implement the model predictive controller (MPC) for a nonlinear process. In this study, the two interacting conical frustum tank level (TICFTL) process is considered and the state space model is developed from the experimental data. The MPC controller is designed based on the state space model. The process of design and implementation of MPC controller in the Matlab & Simulink environment is detailed. The superiority of servo and regulatory controller performances confirms that the MPC is one of the best controllers for the nonlinear process.

Multiloop fractional order PID controller is tuned using Bat algorithm for two interacting conical tank process. Two interacting conical tank process is modelled using mass balance equations. Two Interacting Conical Tank process is a complex system involving tedious interaction. Straight forward multiloop PID controller design involves various steps to design the controller. Due to easy implementation and quick convergence, Bat algorithm is used in recent past for solving continuous non-linear optimization problems to achieve global optimal solution. Bat algorithm, a swarm intelligence technique will be attempted to tune the multiloop fractional order PID controller for two interacting conical tank process. The multi objective optimized multiloop fractional PID controller is tested for tracking, disturbance rejection for minimum Integral time absolute error.

Industrial process loops are mostly interacting in nature. Automation of multi-input-multi-output interacting process involves tedious multiloop PID control design procedure. With the growth of fractional calculus in the field of automation, fractional order PID controller is attempted in this paper. Tuning of the controller parameters involves getting the process model, interaction analysis, tuning the controller parameters between the highly interacting loop and detuning the controller parameters. The design complications can be reduced by simple online tuning of the controller parameters using metaheuristic optimizer. There are many metaheuristic optimizers like bee colony optimizer, ant colony optimizer, cuckoo algorithm, bat algorithm, etc. In the present research work, cuckoo algorithm is used to obtain the multiloop PID controller parameters. The multi-objective optimization function considered in this case involves weighted integral time absolute error of individual loops combined together. The performance is validated by analysing the response for set point change and disturbance rejection.

The Internet of Health things (IoHT) has numerous applications in healthcare by integrating health monitoring things like sensors and medical devices for remotely observe patient’s records to provide smarter and intelligent medicare services. To avail best healthcare services to the users using the e-health applications, in this paper, we propose an IoT with cloud based clinical decision support system for the prediction and observance of Chronic Kidney Disease (CKD) with its level of severity. The proposed framework collects the patient data using the IoT devices attached to the user which will be stored in the cloud along with the related medical records from the UCI repository. Furthermore, we employ a Deep Neural Network (DNN) classifier for the prediction of CKD and its level of severity. A Particle Swarm Optimization (PSO) based feature selection method is also used to improve the performance of DNN classifier. The proposed model is validated by employing the benchmark CKD dataset. Different classifiers are employed to compare the performance of the proposed model under several classification measures. The proposed DNN classifier alone predicts CKD with an accuracy of 98.25% and is further enhanced to 99.25 by PSO-FS method. At the same time, the improved classification performance is verified with higher values of 98.03 specificity, 99.25 accuracy, 99.39 F-score and 98.40 kappa value respectively.

In this paper, trajectory tracking of two-link planar rigid robot manipulator using proportional-integral-derivative (PID) control law based on open source, C++ software has been presented. Traject...

The development and real time implementation of multivariable centralised FOPID (MC-FOPID) controllers for two interacting frustum conical tank level process (TICFTLP) is presented. The modelling and control of TICFTLP is difficult due to its dynamic coupling between inputs and outputs. The black box model is developed from the open loop experimental data using process reaction curve (PRC) method. The multivariable centralised FOPID (MC-FOPID) controller with five tuning parameters is designed based on the steady state gain matrix of the process and then the controller parameters are tuned using bat optimisation algorithm. The comparison of proposed MC-FOPID controller with multiloop PID controller is demonstrated in the simulation study. The simulation results of the controllers are compared in terms of settling time and integral error criteria. It is found that the MC-FOPID controller has better servo and regulatory response than multiloop PID control. The real time implementation of MC-FOPID is done in MATLAB/SIMULINK using USB-based DAQ module.

In this paper, multi-loop adaptive internal model controller (IMC)-based PID is designed for the two interacting conical tank level process (TICTLP). The nonlinear TICTLP is decomposed into linear transfer function matrix around the operating points and the effective open loop transfer function (EOTF) is developed using simplified decoupler. The IMC-based PID controller parameters are obtained for EOTF model using bat optimisation algorithm (BOA). A weighted sum of integral time absolute error is used as control design objective function for multi-loop IMC-PID design which yields faster settling time with minimum over shoot. The fuzzy-based adaptive gain scheduling is used to provide complete control to TICTLP and fuzzy-based adaptive decoupler is implemented to eliminate the dynamic interaction between control loops. The simulation results of proposed controller are compared with conventional ZN-PID, IMC controllers to show the superiority of proposed controller. The simulation response of proposed controller indicates the performance improvement control schemes interns of time domain performance indices, servo tracking, regulatory response and faster settling time.

The design and implementation of proposed multiloop internal model controller for two interacting conical tank process (TICT) is described in this paper. The step response based model is designed and it is approximated into first order plus dead time (FOPDT) system. The control loop interaction and pairing analysed by Relative Gain Array (RGA) and Niderlinski index(NI). The multiloop internal model based controller (IMC) is designed for TICT process. The Controller is implemented and experimental results are presented.

Quadruple Tank process is a multi-input-multi-output process. Mathematical modeling of the MIMO process is computed. State space model is computed from the mathematical model and is converted into transfer function matrices. Fourth order transfer function is approximated into first order plus dead time. Relative Gain Array (RGA) is computed for pairing the input and output. Steady state gain matrix is used for determining illness of MIMO system. Singular value decomposition technique is used to determine condition number to avoid sensitivity problem which arises due to small change of process output. In proposed method, multiloop centralized fractional order PID controller is designed for minimum phase Quadruple Tank process. Simulation studies show the likelihood of the proposed method for the computational analysis of the nonlinear minimum phase interacting process. The experimental results indicate that the developed control schemes work well under servo, regulatory and servo-regulatory conditions.

In the proposed work, multiloop fractional order PID controller is auto tuned using Bee colony optimization for two interacting conical tank process. Control of interacting process is complicated due to the strong interactions. Multiloop PID controller tuning is a tedious and time consuming process. With the application of fractional calculus in the field of science and technology, an attempt is made to design a multiloop fractional order PID controller. The lengthy and difficult procedure of designing multiloop PID controller is more complicated in the design of multiloop fractional order PID controller. Swarm intelligence techniques are gaining popularity in the recent past to get global optimal solution in the field of control systems. Bee colony optimization, a swarm intelligence technique is applied in the present work for auto tuning purpose and validated under tracking and disturbance rejection cases.

Mathematical Model of two interacting conical tank process is derived and the second order process transfer function resulted is reduced to Fractional Order First Order Plus Dead Time (FOFOPDT) system. Normally, any higher order process can be reduced to first order plus dead time. With the recent advancements of fractional calculus in the field of process modeling and control, an attempt has been taken to model fractional order FOPDT system for the process. Identification of the process parameters is not an easy job. Recently, swarm intelligence techniques are used to identify model parameters. The model reduction is obtained using bee colony optimization minimizing root mean square error.