Cloud computing is a promising computing technology utilized in every stage of the business. The cloud offers different services to cloud users from anytime to anywhere, and it is attained with different parameters, like load optimization, resource optimization. Due to the increase in data center, energy consumption has become a major issue in green data centers. The majority of data centers are function using peak load with huge scales. Thus, it is essential for carrying out energy saving in cloud data centers. This paper designed an energy-saving method using fat tree. The proposed techniques optimize the load at different zones of data center and user in the cloud platform. Here, the distribution of load in cloud data centers is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO), which is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. The method utilized different objectives that involve power, load, latency, and bandwidth. With the load distribution, the switching of cloud data center to the desired mode is performed using Actor critic neural network (ACNN). Thus, the dual strategy leads to performance optimization in cloud infrastructure and also in consolidating parallel workload in data centers more effectively. The proposed Taylor-MRFO+ACNN outperformed other methods with minimal energy of 0.553, minimal load of 0.363, and minimal fitness of 0.437, respectively.

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697.

The study examines AI technologies' impact on education, focusing on teaching strategies and student outcomes. AI-driven tools like intelligent tutoring systems and automated grading enhance personalized learning, boost engagement, and reduce administrative tasks for teachers. The research includes qualitative and quantitative data, such as student performance and teacher feedback. While AI greatly benefits tailored learning, challenges like high costs, ongoing maintenance, and the risk of widening educational disparities are highlighted. The study underscores the need for teacher training and equitable access to AI, offering insights for effective AI integration in education.

Over the past ten years, there has been a notable increase in the number of individuals accessing the internet. Positive evaluations serve as social evidence, convincing future purchasers of the product's quality and advantages. They can impact purchase decisions by offering real-world user information. Good reviews increase a product's or brand's trust and reputation. Customers are more inclined to buy from a firm that has received excellent feedback since it demonstrates dependability and contentment. Reviews can be considered user-generated content since they emphasise different applications, features, or advantages associated with a product. This material has the potential to persuade indecisive shoppers. The Yelp website was utilised to scrape feedback data for all Asian restaurants in New York City, which was then trained and assessed using three different models like Navie Bayes, next one is Logistic Regression, and then finally with Support Vector Classifiers. The Logistic Regression classifier outperformed the others by having the lowest proportion of mistakes and the highest Area under the ROC Curve noted as AUC on the receiver operating characteristic curve ROC curve. Commercial insights were gathered by recognising the existence of highly significant phrases while contrasting how they performed to the universal probabilities when the machine learning system was given review data from my restaurant.

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..

In the rapidly evolving telecommunications sector, maintaining profitability and growth depends on customer retention. With the goal of identifying the critical elements influencing customer attrition and creating a useful predictive model, this study offers a thorough investigation of customer churn prediction using a telecom dataset. This study uses a dataset that contains a variety of client features, such as account details, demographic data, and service consumption trends. Here, the data preparation techniques are used to manage anomalies, missing values, and data normalisation. The study uses a range of machine learning methods to forecast churn, such as support vector machines, random forests, decision trees, logistic regression, and gradient boosting. Metrics including accuracy, then precision, also the recall, then F1 score, and also the area under the curve of receiver operating characteristic are used to assess each model’s performance (AUC-ROC). By use of cross-validation and hyperparameter adjustment, we guarantee the models’ resilience and generalizability. Significant churn predictors, including contract type, duration, monthly costs, and customer support interactions, are identified by our investigation. According to the research, month-to-month contract holders who have higher monthly fees and frequent contact with customer service are more likely to experience customer attrition. The model with the highest degree of prediction accuracy is the random forest, which has an AUC-ROC of 0.85, making it the best-performing model. This paper offers a useful foundation for putting churn prediction models into practice in addition to highlighting the important variables causing customer churn in the telecom industry. Telecom firms may lower churn rates by creating focused retention tactics, such personalised offers and better customer care, by proactively identifying at-risk clients. The findings highlight how crucial it is to use machine learning and data analytics to improve client retention and enable commercial success in the telecom sector.

This work presents a Chronological Bald Eagle Optimization (CBEO)-Deep Learning (DL) approach for performing image watermarking. Here, the watermark is implanted in the cover image, by selecting the optimal region in the cover image with the help of the LeNet. Further, the Haar Wavelet Transform (HWT) is utilized in the embedding procedure to improve the robustness of the approach. The trainable parameters of the LeNet used for selecting the optimal region in the cover image are optimized utilizing the CBEO algorithm. Furthermore, the effectiveness of the HWT + CBEO_LeNet is inspected by considering parameters, such as Normalized Correlation (NC)and Peak Signal-to-Noise Ratio (PSNR), and investigations reveal that the proposed HWT + CBEO_LeNet offered high robustness against various noises and attacks and computed a maximum PSNR, NC, and SSIM of 24.989 dB 0.761, 0.969 and obtained least BER value of 0.047, respectively.

Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT[Formula: see text]SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.

Robots are programmable machines built to mimic human actions. One such action is locomotion of robots which is the recent area of research. Two-wheeled robots which diligently stabilize it may contribute to the locomotion of robots in upcoming decades. In this paper, the PID controller and Arduino are utilized to design the self-balancing robot. This paper focuses primarily on developing a controller that will aid the robot and test against several parameters such as position, balance along vertical axis and signals for controlling. The accelerometer and gyroscope sensor values are used to determine the precise position of the robot in 3 dimensional space. The sensor values are sent to the controller which controls the rotation of wheels thus aiding in balancing the robot. The two-wheeled robot turns precisely while navigating through different obstacles as against four-wheeled robots.

Cloud data centers provide incredible services to their customers ubiquitously based on demand and pay-per-use strategy. In virtualized data centers (DC), CPU, RAM, and bandwidth are assigned to a virtual machine (VM) from a group of pooled resources. One of the key issues for virtualized DC is VMs consolidation as it achieves better performance and also reduces the cost. In recent years, researchers have paid more attention to developing the global best solution in DCs. Meanwhile, the global best solution results in a number of redundant migrations and is not desirable for large-scale cloud computing environments. While designing modern software, care must be taken for scaling processes based on consumer demands to slash down the costs of the system. This research proposes an effective strategy based on optimization-enabled VM scaling-based load distribution and optimal switching strategy in the cloud data center. Here, horizontal scaling or vertical scaling is employed to address the overloading complications. The network structure of the cloud is defined depending upon the fat tree model and load distribution of cloud DCs is carried out using pelican Taylor manta ray foraging optimization (P-Taylor MRFO) algorithm by considering multiple objectives, such as power, load, latency, and bandwidth. Based on the load distribution, the switching of the cloud DC to the desired mode is carried out utilizing actor critic neural network. If the system is overloaded, either vertical scaling or horizontal scaling is done based on a predefined threshold elastic scaling using the proposed pelican Adam optimization algorithm (PAOA) based on the horizontal cost that is based on CPU, memory, and hard disk. The PAOA is devised by integrating Pelican optimization algorithm and Adam optimization. However, the proposed model has attained superior results with a minimum load of 0.329, power of 0.532, energy consumption of 0.357, and latency of 0.315.

The Internet of Things (IoT) aims to introduce pervasive computation into the human environment. The processing on a cloud platform is suggested due to the IoT devices' resource limitations. High latency while transmitting IoT data from its edge network to the cloud is the primary limitation. Modern IoT applications frequently use fog computing, an unique architecture, as a replacement for the cloud since it promises faster reaction times. In this work, a fog layer is introduced in smart vital sign monitor design in order to serve faster. Context aware computing makes use of environmental or situational data around the object to invoke proactive services upon its usable content. Here in this work the fog layer is intended to provide local data storage, data preprocessing, context awareness and timely analysis.

Medical imaging provides the visual representation of internal organs of the body which facilitate in diagnosis, monitoring health etc. Previously, the automated diagnosis procedure was done using edge detection and tedious mathematical computations. With the advancement in artificial intelligence, medical imaging is now supporting the diagnosis of cancer, diabetic retinopathy, Detection of Alzheimer’s and Parkinson’s disease, brain injury etc. Diagnosis through medical imaging has reduced the mortality rate drastically especially in the field of cancer. In order to decrease the probability of human error machine learning came into existence. The commonly used machine learning algorithms are K-Nearest Neighbors, Supported Vector Machine (SVM), and Decision Trees etc. But machine learning method has its own limitation of high dependency to the features extracted which depends on many factors. In order to improve the efficiency by removing the dependency on feature deep learning method came into existence. This paper has made a detailed survey on the application of deep learning method in health care service.

SummaryThe major complex issues of routing protocols in heterogeneous wireless sensor network (WSN) are energy balancing and energy efficiency. Though numerous protocols exist in WSN for routing, increasing the system lifetime and to balance the energy consumption still pose a difficult task in the networking scenario. To solve the energy balancing issues and to prolong the network lifetime, an effective routing protocol is designed using the proposed fractional Border Collie optimization (FBCO) algorithm. The proposed FBCO is derived by incorporating fractional calculus (FC) with Border Collie Optimization (BCO), respectively. To make the routing process more efficient, it is significant to group the nodes in the form of cluster such that the formation of cluster is made using Bayesian fuzzy clustering (BFC). The fitness function is designed by taking into account the multiobjective constrictions, like distance, delay, latency, route link time (RLT), and energy. With these objective factors, the optimal value is computed for each solution in the search space such that the computation of the optimal value enables the routing protocol to enhance energy consumption and network lifespan. The performance achieved by the introduced FBCO‐based routing procedure based on delay is 0.3470 s, latency is 0.3142 s, throughput is 97.58%, and residual energy is 0.1726 J by considering 100 numbers of nodes.

Brain tumor identifications are the most common issues for recent scenario of health care community. The accurate discovery of various brain abnormalities is highly essential for treatment planning that can minimize the fatal results. Performance can be measured only through soft computing techniques. Besides being accurate, these systems must touch rapidly in order to apply themfor day-to-dayapplications. Now a day’s manycomputerizedtechniques are available for this desirableperformance measures, but no clear discrimination between these techniques about the aptness forrelevant applications. Lot of reports insists its work to be greater but a detailed analysis is missing in these works. In thispaper, awidespreadrelative analysis is focused to illustrate the qualities and limitations of various existing methods. The main goal of this work is to emphasizethe variety of automated methods which can ultimatelyserveto developing novel ideas forsolving the health care issues of the current society.

In many standard applications like peer-to-peer systems, large amounts of data are distributed among multiple sources. Analysis of this data and identifying clusters is difficult due to process, storage, and transmission costs. A GD Cluster, a general fully decentralized clustering method, which is capable of clustering dynamic and distributed data sets. Nodes continuously cooperate through decentralized gossip-based communication to maintain summarized views of the data set. We customize GD Cluster for execution of the partition-based and density-based clustering methods on the summarized views, and also offer enhancements to the basic algorithm. Coping with dynamic data is made possible by gradually adapting the clustering model. We Proposed a Decentralized Clustering Frame Work Search Engines. Coping with dynamic data is made possible by gradually adapting the clustering model. Our experimental evaluations show that GD Cluster can discover the clusters efficiently with scalable transmission cost, and also expose its supremacy in comparison to the popular method LSP2P.

One of the driving forces behind the industrial revolution was the invention-more than a century ago -of the electric motor. Its widespread use for all kinds of mechanical motion has made life simpler and has ultimately aided the advancement of humankind. And the advent of the inverter that facilitated speed and torque control of AC motors has propelled the use of electric motors to new realms that were inconceivable just a mere 30 years ago. Advances in power semiconductors-along with digital controls-have enabled realization of motor drives that are robust and can control position and speed to a high degree of precision. he use of AC motor drives has also resulted in energy savings and improved system efficiency. This paper reviews the development and application of inverter technology to AC motor drives and presents a vision for motor drive technology. The development of more efficient, more powerful electric motor drives to power the demands of the future is important for achieving energy savings, environmentally harmonious drives that do not pollute the electrical power system, and improving productivity. Yukawa wants to be an integral this.

In this article, the segmented brain tumor region is diagnosed into mild, moderate, and severe case based on the presence of tumor cells in the brain components such as Gray Matter (GM), White Matter (WM), and cerebrospinal fluid (CSF). The modified spatial fuzzy c mean algorithm is used to segment brain tissues. The feature Local binary pattern is extracted from segmented tissues, which is trained and classified by ANFIS Classifier. The performance of the proposed brain tissues segmentation system is analyzed in terms of sensitivity, specificity, and accuracy with respect to manually segmented ground truth images. The severity of brain tumor is diagnosed into mild case if the segmented brain tumor is present in the grey matter. The severity of brain tumor is diagnosed into moderate case if the segmented brain tumor is present in the WM. The severity of brain tumor is diagnosed into severe case if the segmented brain tumor is present in the CSF region. The immediate surgery is required for severe case and medical treatment is preferred for mild and moderate case.

Magnetic Resonance Imaging (MRI) is an advanced medical imaging technique that has proven to be an effective tool in the study of the human brain. In this article, the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification. Histogram equalization is used in enhancement stage, gray level co-occurrence matrix and wavelets are used as features and these extracted features are trained and classified using Support Vector Machine (SVM) classifier. The tumor region is detected using morphological operations. The performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The proposed system achieved 0.95% of sensitivity rate, 0.96% of specificity rate, 0.94% of accuracy rate, 0.78% of PPV, and 0.87% of NPV, respectively. (c) 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 297-301, 2015

Indefinite and uncontrollable growth of cells leads to tumors in the brain. The early diagnosis and proper treatment of brain tumors are essential to prevent permanent damage to the brain or even patient death. Accurate data regarding the position of the tumor and its size are essential for effective treatment. Hence, an entirely computerized automatic system to provide accurate tumor data is compulsory for physicians. Such developments are necessary to diagnose brain tumors during brain surgery. Brain magnetic resonance (MR) images are proposed for the detection and segmentation of the tumor region via a completely automatic and highly accurate method. The approach discussed in this paper employs an adaptive neuro fuzzy inference system (ANFIS) based on the automatic seed point selection range. The pixels intensity of the proposed algorithm is not dependent on the tumor type. The tumor's segmentation results are evaluated based on various criteria, including similarity index (SI), overlap fraction (OF), extra fraction (EF) and positive predictive value (PPV), which corresponded to values of 0.817%, 0.817%, 0.182%, and 0.817%, respectively, in this study. These results indicate that the approach proposed in this study performs better compared to many conventional processes. The significance of this work is the differentiation of brain abnormalities from the healthy brain tissue.