Industrial machinery (IM) holds significant importance in the field of research especially for prototyping and experimentation, large-scale data generation, testing and quality control, simulation and modeling, precision (P) and accuracy (A), automation and efficiency. Thus, it is important to keep track of the health of IM at regular intervals for the purpose of tracking the equipment lifespan, maintenance costs, unscheduled downtime, efficiency and performance, and safety hazards, etc. for taking preventive measures at the earliest. So, different approaches such as federated learning (FL), quantum computing (QC), machine learning (ML), deep learning (DL), edge computing, etc. can be utilized to track the health status of IM. In this work, Quantum ML (QML) based approach is focused to monitor the fitness of industrial grinding machine (IGM) as ok (K) or not ok (NK). This work is focused on Quantum Gradient Boosting Model (QGB), Quantum Feature Mapping with Support Vector Machine (QSM) and Hybrid Quantum Model (HQM) to track the fitness of IGM. These models are also compared with classical SVM, Random Forest (RF), GB and XGBoost (XGB) for effective comparison. These models are compared in terms of classification A (in

The task of influential spreaders selection is used in different fields of research for different purposes. The selection of influential spreaders is an important issue in social networks. This paper discusses a novel multi-valued ranking framework for selecting the key communicators in a social network. At first, it combines the eigenvectors of both adjacency and Laplacian matrices. After that, it integrates the community structure to obtain influential spreaders. The proposed approach simulates the diffusion dynamics of the selected nodes by the help of the Independent Cascade (IC) model. The mean influence capacity (in %) serves as a metric for comparing the proposed method with state-of-the-art techniques. The results show the superior performance of the proposed approach.

In this information age, the dissemination of information has a pivotal function in creating the adoption of new technologies, marketing of products, etc. However, the localization of information in a particular region is also a problem of the same importance to be addressed. For example, once a rumor is detected, it has to be localized at some parts of the network to obstruct its spreadability. This paper focuses on achieving information localization. The study says that localization can be achieved by manipulating the IPR value achievable through perturbation of the connections. In this paper, a Particle Swarm Optimization (PSO) based perturbation approach is proposed that passes through the steps of evolution to generate a graph with optimal IPR from the original graph. Hence, it guarantees a higher state of information localization in the output graph. The empirical results confirm a better localization by the proposed method than some existing methods. We use average optimal IPR as a parameter of evaluation.

Edge computing (EC) and the Internet of Things (IoT) have transformed healthcare, especially real-time patient monitoring. This comprehensive study explores the architecture, implementation, and clinical impacts of edge computing in the healthcare sector. It assesses workloads, network conditions, and the capabilities of devices for real-time processing. This paper proposes a Healthcare Metropolitan Area Network (HMAN) architecture to support health-related applications and services across the city. Unlike existing models, we propose a Data Flow Offloading (DFO) algorithm that introduces a multi-layer hierarchical structure for handling high volumes of patient data. It ensures scalability by utilizing distributed clouds in extreme overload situations like pandemics. The proposed approach attains a faster system response time than existing models.

Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge servers. So, a variety of offloading strategies are offered in previous research to enhance the efficiency of SDs by offloading their workloads to neighboring cloudlets or distant cloud computing resources. Consequently, optimization techniques are crucial for scheduling computation offloading tasks in (ED) networks. To the greatest extent of current understanding, an extensive analysis of meta-heuristic-based computation offloading (CO) techniques in the EC has not been systematically surveyed. This systematic survey creates an inventory of CO in EC and further summarizes the most advanced recently available EC technologies. In this survey, three types of optimization techniques are examined: Lyapunov, Meta-heuristic, and Convex. The objective functions, application domains, offloading methods, evaluation techniques, merits, and demerits of the existing algorithms are summarized. The proposed survey undergoes four main CO processes: task scheduling, priority-aware task scheduling, load balancing, and edge server selection. In addition, this research provides a comparative analysis of the offloading techniques. Subsequently, unresolved problems and upcoming unfocused or inadequately covered research tasks are explored.

Edge computing is a technology that reduces energy consumption and traffic congestion for delay-sensitive service requests. It involves processing data locally or in adjacent edge data centers, meaning data travels shorter distances than it would under standard cloud architecture. However, unlike the cloud, fog/edge resources are resource-constrained, diverse, and dynamic, which makes resource management difficult. This paper proposes a priority scheduling approach for healthcare system. The experiment shows that the proposed approach outperforms Patient Tracking System with Resource Allocation (PTS-RA) under the evaluation parameters: CPU utilization, and energy consumption.

Irrigation system (IS) is considered as a crucial component in the human society. It plays a crucial role for the supply of water to the cultivation fields. So, it is very much essential to predict the water pumping requirement in IS. In this work, an Internet of Things (IoT)-assisted machine intelligence (MI)-based approach is proposed for the prediction of water pumping requirement in IS. This work is focused on the machine learning (ML)-based models such as random forest (RF), CatBoost (CB), K-nearest neighbors (KNN), and stochastic gradient descent (SGD) to perform the prediction. In this work, the water pumping requirement is monitored using IoT-assisted sensors. Here, the water pumping requirement and non-requirement cases are represented using 1 and 0, respectively. This work is carried out using cross-validation (CRV) by taking the number of folds (NFL) as 3, 5, and 10. These models are evaluated using classification accuracy (CA). This work is implemented using Python-based Orange 3.32.0.

Meta-heuristic algorithmic development has been a thrust area of research since its inception. In this paper, a novel meta-heuristic optimization algorithm, Olive Ridley Survival (ORS), is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. A major fact about survival of Olive Ridley reveals that out of one thousand Olive Ridley hatchlings which emerge from nest, only one survive at sea due to various environmental and other factors. This fact acts as the backbone for developing the proposed algorithm. The algorithm has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. The phases are mathematically modelled and implemented along with suitable input representation and fitness function. The algorithm is analysed theoretically. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested. Also, to study the efficacy of ORS on recent complex benchmark functions, ten benchmark functions of CEC-06-2019 are evaluated. Further, three well-known engineering problems are solved by ORS and compared with other state-of-the-art meta-heuristics. Simulation results show that in many cases, the proposed ORS algorithm outperforms some state-of-the-art meta-heuristic optimization algorithms. The sub-optimal behavior of ORS in some recent benchmark functions is also observed.

Choosing skilled and talented teachers for assigning appropriate subjects as per the needs of students is always a challenging task. To make this problem so simple, the assignment method is used by choosing the best teachers with high rating for assigning the subjects. In this work, we have proposed an assignment technique to solve this educational course assignment problem. The problem was also solved with the Hungarian method based on the data obtained from five teachers from six teachers to teach five different subjects so that minimization can be done for the total number of class hours in a department in the selected institution. From the comparison analysis of the two methods, it is observed that both methods show the same optimal solution of 166 hours; however, the proposed method can solve the problem in fewer steps. The simulation is also performed using MATLAB. From the simulation, it is observed that the proposed method shows lesser computation time than the Hungarian method.

Communication in any system is grounded in its interconnection pattern. This pattern allows the components of a system to communicate among themselves and work together to achieve the system-level goal. Information flow within a social system or any organizational setup plays a pivotal role in its growth. Availability of information across most parts of the network is often essential for advancing a social cause or organizational benefit. Selecting a seed set of influencers from where the information has to be initiated to maximize the diffusion is a crucial task. In this context, this paper proposes a three-level scoring framework: (i) inter-community edge-based score (global level), (ii) community-influence-oriented score (gateway level), and (iii) intra-community-oriented score (local level). Each node is assigned with certain importance values in all three levels, i.e., global level, gateway level, and local level. We utilize the core–periphery structure of the communities to calculate the gateway- and local-level scores of a node. Nodes with high overall scores (CKI_Score) from all the communities are combined to form a seed set, denoted as ‘S’. The simulation of the diffusion process is implemented using the independent cascade model. The performance of the proposed algorithm is assessed through the average influence capacity (in

Understanding different aspects of information spread have been a crucial direction of research in information and social sciences. Recent studies are found to be focused on accelerating and controlling the spread of information. One such study is to control the spread of information by manipulating the Principal Eigen Vector (PEV) of the network adjacency matrix. In this paper, Inverse Participation Ratio (IPR) is used to measure the localization of PEV. The studies say that maximum IPR leads to a high localization state of the network. In literature, IPR maximization is shown to be a hard problem. Therefore, we use approximation approach of solution to this problem by employing metaheuristic algorithms. In this direction, we propose graph versions of three Rao’s metaheuristic variants: Rao’s Algorithm, Jaya Algorithm, and Teaching–Learning-Based Optimization (TLBO) Algorithm for IPR maximization. Four evaluation parameters: Average Optimal IPR, Factor Improvement (IPR), Average Modification

Communicable disease pandemic is a severe disease outbreak all over the countries and continents. Swine Flu, HIV/AIDS, corona virus disease-19 (COVID-19), etc., are some of the global pandemics in the world. The major cause of becoming pandemic is community transmission and lack of social distancing. Recently, COVID-19 is such a largest outbreak all over the world. This disease is a communicable disease which is spreading fastly due to community transmission, where the affected people in the community affect the heathy people in the community. Government is taking precautions by imposing social distancing in the countries or state to control the impact of COVID-19. Social distancing can reduce the community transmission of COVID-19 by reducing the number of infected persons in an area. This is performed by staying at home and maintaining social distance with people. It reduces the density of people in an area by which it is difficult for the virus to spread from one person to other. In this work, the community transmission is presented using simulations. It shows how an infected person affects the healthy persons in an area. Simulations also show how social distancing can control the spread of COVID-19. The simulation is performed in GNU Octave programming platform by considering number of infected persons and number of healthy persons as parameters. Results show that using the social distancing the number of infected persons can be reduced and heathy persons can be increased. Therefore, from the analysis it is concluded that social distancing will be a better solution of prevention from community transmission.

Recently, research in the spread of information is found to be a crucial domain in the field of social network analysis. Understanding information spreadability and controllability are the two aspects of the same study. One of the important network parameters, the Inverse Participation Ratio (IPR) of a network adjacency matrix can measure the state of information localization. Higher the value of IPR, the higher the state of localization. This paper proposes a new perturbation approach based on k-shell decomposition to meet the optimal IPR. The proposed Shell-based Perturbation (SP) approach is compared with one of the state-of-the-art approaches: Random Perturbation (RP). The result confirms the superior performance of the proposed SP approach over the existing RP approach.

Link prediction is a crucial task in social networks where the objective is to predict the missing links and upcoming links among the social entities. This paper considers the problem of link prediction as a classification problem. The network features are embedded into a matrix or dataset where the columns are network measures and rows are the edges represented by edge source and destination. Here, we consider the combination of local, quasi-local, and global measures as features. The dataset is divided into training and testing sets. Four supervised learning algorithms: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are used for this task. The results show that the accuracy of XGBoost outperforms the rest three algorithms. The Shortest_Path is found to be the most contributing feature of this prediction by SHapley Additive exPlanations (SHAP). By this analysis, it is found that Shortest_Path has an interaction effect with Destination core number (Core_Num_Dst).

Nowadays, wireless sensor networks (WSNs) are often used in industrial settings to gather data regarding the different machines in use. There are numerous sensors such as pressure gauges, temperature sensors, dust sensors, etc. that are used to gather data in real-time. These sensors pass on the information to the base station from where it is further sent to the server. The data collected are then analyzed to determine the working condition of the different machines used in industry. The data obtained via the sensors are huge and cannot be manually examined by a person. Rather the data is fed to some machine intelligence (MI) systems which analyze the data and derive suggestions based on prior learning experience. In this work, an MI-based approach is used for the prediction of water pump risk in industrial WSN. This work is focused on several supervised machine learning (ML) based models such as Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM), and Naive Bayes (NB) to perform predictions. This work is carried out using cross validation by considering the number of folds (NFD) as 2, 3, 5, and 10. These models are evaluated using the classification accuracy (CA) performance metric. This work is implemented using Python-based Orange 3.26.0.

In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme.

Every year, the worldwide health record reports enormous cases of deaths due to heart disease. The advancement in healthcare system has tackled these issues in some extent but still the severity of heart disease persists in the society. In near past, huge amount of effort has been made to incorporate computational techniques like machine learning based approaches to handle this issue in an effective way. Several research articles report the use of machine learning approach for early prediction of the heart disease from the data of different clinical attributes obtained from clinical investigations/tests. Specifically, the supervised machine learning approaches used for this purpose prepares the model from the available datasets collected from the patients’ health records with their known status of suffering from heart disease or not, and the model can predict a person is suffering from heart disease or not. In the same line, we apply some standard classifiers on the heart disease dataset collected from UCI machine learning repository. Unlike existing proposals, we propose a distribution preserving train-test splitting and after that apply the classifiers on it. Likewise, we also consider the ensemble classifiers for this purpose. The result shows that Naïve Bayes Classifier (NB-C) performs best among all individual classifiers under consideration according to Accuracy, Precision, Recall, and F1-score. We also prepare an ensemble (ALN-C) of three best individual classifiers obtained from the evaluation i.e., Artificial Neural Network Classifier (ANN-C), Logistic Regression Classier (LR-C), and Naïve Bayes Classifier (NB-C) and compare it with two existing ensemble methods: AdaBoost, and Random Forest. For the proposed distribution preserving train-test splitting, ALN-C ensemble method outperforms AdaBoost, and Random Forest according to Accuracy, and F1-score.

Internet of medical things (IoMT) plays an important role nowadays to support healthcare system. The hospital equipment’s called as medical things are now connected to the cloud for getting many useful services. The data generated from the equipments are sent to the cloud for getting the desired service. In current scenario, most hospitals collect many images using equipments, but these equipments have less computational capability to process the huge generated data. In this work, one such equipment is considered which can take the human eye images and send the images to the cloud for detection of cherry red spot (CRS). CRS disease in eyes is considered as one of the very dangerous disease. The early diagnosis of CRS disease needs to be focused in order to avoid any adverse effect on human body. In this paper, a machine intelligence based model is proposed to detect the CRS disease areas in the human eyes by analyzing several CRS disease images using IoMT. The proposed approach is mainly focused on fuzzy rule-based mechanism to carry out the identification of such affected area in the eyes in cloud layer. From the results, it is observed that the CRS disease areas in the eyes are detected well with better detection accuracy and lower detection error than k-means algorithm. This approach will help the doctors to track the exact position of the affected areas in the eye for its diagnosis. The simulation is performed using socket programming written in Python 3 where a cloud server and a client device are created and images are sent from the client device to the server, and afterwards the detection of CRS is performed at the server using MATLAB R2015b. The proposed method is able to provide better performance in terms of detection accuracy, detection error and processing time as 94.67%, 5.33% and 1.1481% units respectively on an average case scenario.