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The traditional healthcare systems require more data storage and not a quick responsive mode. The new era of intelligent healthcare systems uses advanced frameworks and devices to work with Internet of things (IoT), Cloud Computing, edge computing devices, and the Internet of Medical Things (IoMT). These technology adoptions may drastically increase healthcare systems performance and are available anytime and anywhere to replace traditional medical methods – the organizations in the network exchange the patient data that has been gathered. The healthcare systems are therefore susceptible to a variety of threats. The attacker may exploit many attacks during the transmission. This paper introduces a novel mechanism of A Secure Robust Privacy-Preserving Authentication and Key Agreement Framework for Mobile Healthcare Applications. The proposed framework collects the patient’s data using wearable devices called sensors, and the collected data is shared with various entities using a secure mechanism. The proposed framework uses Elliptic Curve Cryptography (ECC). Digital sign is created and validated through the Elliptic Curve Digital Signature Algorithm (ECDSA). Security properties of the proposed framework are analyzed through standard model checking tool Automated Validation of Internet Security Protocols (AVISPA). The suggested protocol is simple to develop and can withstand network-related threats.

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

The adequate aging hypothesis seeks to help people live longer, healthy lives. Diabetic patients who stay remotely need an infrastructure to monitor them continuously and provide timely treatment. Ambient assisted living (AAL) encourages the establishment of solutions that may help optimize older people’s assistive environment while also reducing their impairments. The blood glucose levels of diabetic patients are continuously monitored by gold oxide sensors placed over the human body. The signals associated with the glucose levels in the human body are plotted over a spectrogram image using the short-time Fourier transform, which is further classified using the deep learning model based on finetuned AlexNet, which has employed random oversampling and batch normalization for better precision in the results. The model classifies the spectrogram images as low and high glucose levels and normal glucose levels. Thereby alarming the caretakers for effective treatment of the individuals. Body area networks (BANs) gather information from biosensors and send it to a domain controller to assist caretakers and physicians in recommending the physical exercises for their clients. Evaluation criteria such as sensitivity and specificity, precision, and Mathew’s correlation coefficient are used to assess the effectiveness of the proposed model in this current diabetes study. The cross-validation of the model at multiple folds is being evaluated to analyze the performance. It is evident from the obtained results that the proposed model has exhibited an acceptable performance in precisely sensing the individuals with abnormal glucose levels.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

Nowadays, in most countries, the most dangerous and life threatening infection is Chronic Kidney Disease (CKD). A progressive malfunctioning of the kidneys and less effectiveness of the kidney are considered CKD. CKD can be a life threatening disease if it continues for longer period of time. Prediction of chronic disease in early stage is very crucial so that sustainable care of the patient is taken to prevent menacing situations. Most of the developing countries are being affected by this deadly disease and treatment applied for this disease is also very expensive, here in this paper, a Machine Learning (ML)-positioned approach called Neuro-Fuzzy model is used for prediction belonging to CKD. Based on the image processing technique, fibrosis proportions are detected in the kidney tissues. It also builds a system for identifying and detection of CKD at an early stage. Neuro-Fuzzy model is based on ML which can detect risk of CKD patients. Compared with other conventional methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), the proposed method of this paper — ML-based Neuro-Fuzzy logic method — obtained 97% accuracy in CKD prediction. This method can be evaluated based on various parameters such as Precision, Accuracy, Recall and F1-Score in CKD prediction. From the results, the patients having high risk of chronic disease can be predicted.

The authors have requested that this preprint be removed from Research Square.

An earthquake is one of the most massive natural disasters which happens unexpectedly shaking the earth's surface. Due to earthquakes, not only infrastructure but also buildings get damaged thereby affecting lifestyle. For the early-stage prediction of the earthquake impact, machine learning can play a vital role, and this entails the novelty of the work. For this perception, six different machine learning classifiers namely Artificial Neural Network, Random Tree, CHAID, Discriminant, XGBoost Tree, and Tree-AS on six datasets of different regions of India. All the algorithms have been applied to each dataset. The objective of the research is to predict the value of magnitude for the future earthquake in India and nearby regions from the historical data on earthquakes. From the result, It has been observed that for Andaman & Nikobar dataset XGBoost Tree achieved the highest accuracy with 99.10%, for the Gujarat dataset Tree-AS achieved the highest accuracy with 91.67%, for the North India dataset Artificial Neural Network achieved the highest accuracy with 99.13%, for North East India dataset XGBoost Tree achieved the highest accuracy with 99.04%, for Nepal-UP-Bihar dataset XGBoost Tree achieved the highest accuracy with 99.01%, for Nearby India’s Country dataset XGBoost Tree achieved the highest accuracy with 92.12%. From all the results, it has been noted that XGBoost tree classifier performed well in most datasets., the Curve has been made between magnitude & gap, magnitude & magnitude error, and magnitude and depth error for finding the mathematical relation between them.

Nanomachines that can run tasks and molecular communication (MC) for interconnecting nanomachines have been made possible by advances in nanotechnology. Different techniques of Automatic Repeat Request (ARQ) protocols have been devised in MC for acquiring the necessary molecules, although retransmission of duplicate copies requires special attention. This work presents a diffusion-based MC with a feedback loop to improve MC performance. Based on the received ACK(s) or NACK(s), the Channel state determines if the state is good (G) or bad (B), and then single path or multipath procedures are used to the ‘G' and ‘B' states. The results of the MATLAB simulation illustrate how the systems compare in terms of communication rate, the possibility of complete transmission of the desired molecules, and energy consumption. When the channel state is bad, the multipath approach performs better, but when the channel state is perfect in MC, the single path strategy performs better.

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical [Formula: see text]-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.

The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.

Buyers to whom a product would be introduced should check it to make better choices about the item. To arrive at a specific finding, various viewpoint mining methods have been suggested. Several recent developments in machine learning, especially deep learning, have led to considerable progress in solving sentiment classification problems. To achieve valuable scores as poor supervision indicators, this research work suggests an innovative deep learning system for performing product review based emotion classification. To achieve a high-level representation, one needs to learn the embedding before applying a classification layer on top of the embedding.

Wireless Sensor Networks (WSN) is most widely used in many applications for various purposes. WSN consists of sensors, nodes, etc. In WSN, every node requires constant energy to transfer the data from the source node to the destination node. Several challenges are identified in WSN, such as energy at nodes, accurate routing, data loss, etc. The WSN aims to better transmit data between nodes by satisfying the user requirements without any threats. Sensor nodes are considered as small devices that will work on battery power. These are interconnected in the network, and these are distributed by using devices. In this paper, A Novel Routing Protocol (NRP) is introduced to overcome the various routing issues in WSN and maintaining the energy levels constantly. Results show the performance of NRA and display the accurate results.

Often cloud providers and cloud clients illustrate several constraints and thus allocation of resources in a heterogeneous cloud is a difficult job. As the traffic flow is quite subjective and Client necessities and applications size vary regularly, the major challenge and concern is to map the external job requests to available virtual machines. To reduce the gap among regularly altering client requirements and existing resources, Client-Awareness Allocation of Resources and Scheduling of jobs in cloud by using social group optimization (SGOCARAJS) is proposed. This algorithm is mainly split into two phases namely allocation of resources using SGO and shortest job first scheduling. The main aim is to map the jobs to virtual machines of cloud group to attain higher client satisfaction and lowest makespan time. Experiments are conducted on datasets and results are compared with present scheduling techniques. This model proved that this algorithm outrun the available algorithms based on concerned metrics.

Effective resource distribution to regulate load uniformly in heterogeneous cloud environments is crucial. Resource allotment which is taken after capable task scheduling is a critical worry in cloud environment. The incoming job requests are assigned to resources equally by load balancer in such a way that resources are utilized effectively. Number of cloud clients is very great in number, degree of approaching job requests is uninformed and information is tremendous in cloud application. Resources in cloud environment are constrained. Hence, it is not easy to deploy different applications with unpredictable limits and functionalities in heterogeneous cloud environment. The proposed method has two phases such as allocation of resources and scheduling of tasks. Effective resource allocation is proposed using social group optimization algorithm and scheduling of tasks using shortest-job-first scheduling algorithm for minimizing the makespan time and maximizing throughput. Experimentations are performed for accurate simulations on artificial data for heterogeneous cloud environment. Experimental results are compared with first-in, first-out and genetic algorithm-based shortest-job-first scheduling. Validity of the proposed method noticeably gives improved performance of the system in provisions of makespan time and throughput.

To adequately take an interest in the current coordinated partnerships, associate companies ought to have the capacity to impart computerized resources to different collaborators, while guaranteeing their advanced resources is shielded from unseemly access. In the current literature, a significant measure of research on unified access control with regards to one company has been completed. Be that as it may, investigation on decentralized access control in a shared domain is rare. A model of a decentralized access control framework is implemented, which strengthens auditability, unchanging nature, and uniformity in a cooperation domain utilizing the multi-chain blockchain. The model was created to assess two essential measurements: throughput and response. To accomplish this, various analyses have been performed to quantify the two measurements on Amazon Web Services.

The main aim of this work is to present a multi-faceted cost model depending on server auto-scaling which is based on task requests waiting in queues. To survive for long period in the market, cloud service providers can adopt task—server consolidation. The reason for heavy carbon emanation is power used up on cooling infrastructures of cloud. The present work proposes a dynamic cost model with incentive, fine based on task request time and effect of resource sharing between various tasks to achieve maximum usage of server. Task requests are described at the time of registering with cloud service provider. Cloud clients decide about their cost for service and incentives for the task. Various features are recognized in this profile that includes many weights for the service charge. Possibility of users getting back is also estimated.

Upholding quality of service (QoS) parameters while ranking cloud-based Virtual Machines (VMs) that deliver the same service is a challenging task which has been addressed by prior approaches like VM resource deep analytics (RDA). But these approaches fail to consider the heterogeneous aspect of the VMs where higher resource-centric VMs tend to offer sublime performance and lower resource-centric VMs offer nominal throughput. This can also influence the VM RDA ranking algorithms where the former tends to be at the top of the ranks while the latter at margins. To counter this effect and to create an equal footing to most VMs and optimize the rankings despite the VMs varying resource centricity, we propose a VM packaging algorithm that addresses the heterogeneous aspect. We considered a maximization problem of Virtual Machine where each machine is assigned P pages of memory, a set of m servers, a group of V virtual machines, such that a version of the problem consists of one server which is developed by using the dynamic programming solution to deploy all VM instances simultaneously and consider their ranking despite their heterogeneous aspect. Aided with this new algorithm, we intend to reduce the delays and overheads experienced with the usage of heterogeneous complexity of VMs and tend to deliver an efficient ranking solution.