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In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results. [ABSTRACT FROM AUTHOR]

One of the key problems for researchers and network managers is anomaly detection in network traffic. Anomalies in network traffic might signal a network intrusion, requiring the use of a quick and dependable network intrusion detection system. Intrusion detection systems based on artificial intelligence (AI) techniques are gaining the interest of the research community as AI techniques have evolved in recent years. This research proposes a novel method for anomaly detection using artificial neural networks (ANNs) optimized using cuckoo search algorithm. For simulation purposes, the NSL-KDD dataset has been utilized with a 70:30 ratio where 70% of data is used for training and the remaining 30% is used for testing. The proposed model is then evaluated in terms of mean absolute error, mean square error, root-mean-square error, and accuracy. The results of the proposed work are compared with standard methods available in the literature including fuzzy clustering artificial neural network (FC-ANN), intrusion detection with artificial bee colony, neural network intrusion detection system, and selection of relevant feature. The results clearly show that the proposed method outperforms the listed standard methods. [ABSTRACT FROM AUTHOR]

Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in the proposed algorithm. For the said purpose, the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences, including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results. [ABSTRACT FROM AUTHOR]

Numerous patients died every year due to the leading causes of deaths all over the world and burn injuries are one of them. Burn injury cases are most viewed in low and middle-income countries (LMIC). Researchers show great interest to classify the burn into different depths through digital means. In Pakistan, at provisional level, it's really a significant issue to categorize the burn and its depths due to the non-availability of expert doctors and surgeons; hence the decision for the correct first treatment can't be made, so this may cause a serious issue later on. The main objectives of this research work are to segment the burn wounds and classification of burn depths into 1st, 2nd and 3rd degrees respectively. A real-time dataset of burnt patients has been collected from the burn unit of Allied Hospital Faisalabad, Pakistan. The dataset used for this research task contains 450 images of all the three levels of burn depths. Segmentation of the burnt area was done by the use of Otsu's method of thresholding and feature vector was obtained through the use of statistical methods. We have used the Deep Convolutional Neural Network (DCNN) to estimate the burn depths. The network was trained by 65 percent of the images and the remaining 35 percent images were used for testing the accuracy of the classifier. The maximum average accuracy obtained by using the Deep Convolutional Neural Network (DCNN) classifier is reported round about 79.4% and these results are the best if we compare them with previous results. From the obtained results of this research work, non-expert doctors will be able to apply the correct first treatment for the quality evaluation of burn depths. [ABSTRACT FROM AUTHOR]

Routing protocols plays a key role in coherent use of resources in Internet of Things (IoT). The more efficient routing scheme is, the more efficiently will nodes communicate with each other. In this paper, we present presents an efficient cluster based routing protocol for IoT. The protocol aims at increasing lifetime and stability of networks. The paper introduces a new routing scheme Modified-Percentage LEACH Protocol based on existing protocol namely Percentage LEACH. Extra amount of energy wastage is alleviated by reducing communication between Cluster Heads (CHs) and sink. Thus, it increases the lifetime of sensor nodes in IoT communication networks. It is achieved by introducing threshold calculation on each CH, which is used as basic criteria for CH election and selection. The protocol also takes into account distance parameter of nodes from sink. This protocol helps plummeting energy wastage which results in more lifetime and high throughput. The depicted protocol is compared with its parent protocol and simulation results shows high throughput and outperforms its parent protocols. [ABSTRACT FROM AUTHOR]

Personal health record systems (PHR) assists people to access, manage and share their own health information. PHR is getting increasingly popular these days. However, there is not even a single PHR system being deployed in Saudi Arabia considering the special needs of Saudi Arabian society. Before a PHR system is deployed, it is mandatory to know the awareness and willingness of the people so that usability of the system can be defined. In this research, we have conducted a survey in Saudi Arabia to know the level of awareness of PHR systems and the willingness of people to adopt and use such systems. In order to conduct the survey, a total of one hundred and sixty four individuals from different age groups were selected randomly keeping in perspective the target population. For reaching to the ends that stretch beyond the immediate data alone, we have used the inferential statistics for our research work. During our inferential analysis, we have selected the cross-tabulation method for comparing two different variables. The cross-tabulation of categorical and ordinal data is a good tool for summarizing demographic information and searching for blueprints. We used the Chi-Square tests to find out whether an association (or relationship) between 2 categorical variables in a sample is likely to contemplate a substantial association between these 2 variables in the population. The results indicate that the level of awareness about PHR systems is still low in the Saudi society but the people are willing to use a personalized electronic system to manage their own health. The results further indicate that the availability of the internet is a major barrier to the realization of PHR systems. These results encourage to adopt a Cloud-Dew Architecture based Layered PHR system that goes beyond the trivial network/storage/service concept to a new micro-service concept offering high scalability and availability in vertically distributed computing hierarchy. [ABSTRACT FROM AUTHOR]

Personal health records (PHR) system serves not just as static repositories for data but it also combines knowledge and software tools with patients' data that results in empowering patients to become active participants in their own healthcare management by providing their own medical history. In this research, a cloud-dew Architecture based Layered Provenance Framework for PHR system is proposed that goes beyond the trivial network/storage/service concept to a new micro-service level concept offering high scalability and availability in vertically distributed computing hierarchy. It pushes the frontiers of computing applications, data, and low-level services away from centralized virtual nodes to the end users. In response to this new micro-service model, a study was conducted of initially scrutinizing the cloud-dew architecture and ascertaining a list of requirements for the collection of provenance. We have observed the different layers of the cloud-dew architecture for identifying the requirements while keeping in view the several characteristics of cloud-dew architecture such as abstraction, modularity, etc. A lightweight and cost-efficient provenance framework is designed and established for its accomplishment. In addition, this proposed framework also provides services such as storage, query, and visualization of provenance besides highlighting the identified list of requirements. To measure the benefits of enabling provenance in Dew Computing, the pre-collected provenance and services of the proposed framework are utilized. [ABSTRACT FROM AUTHOR]

Heterogeneous data on distributed computing sources are growing day by day. To manage the data from the distributed sources into a distinct type of application like mobile, cloud, desktop, web etc. is a challenging issue in the global information systems, particularly for cooperation and interoperability. This paper proposes a Data Service Framework, which integrates the data from distributed sources such as databases, Simple Object Access Protocol (SOAP) based web services and flat files, and performs create, read, update and delete (CRUD) operations on it through Representational State Transfer (REST) services over the Hyper Text Transfer Protocol (HTTP). The proposed data service framework also supports java database connectivity (JDBC). Detailed description of the proposed framework and experimental results are reported in this paper. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11042-020-08768-y B Correction to: Multimedia Tools and Applications (2020) 79:34545-34568. b https://doi.org/10.1007/s11042-020-08768-y The original publication of this article contains incorrect spelling of the university name of the fourth affiliation. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]