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The purpose of this paper is to propose a novel combined cooling, heating, and power generation system driven by evacuated tube solar collectors for residential applications. Accordingly, evacuated tube collectors are employed to provide sufficient heat for cooling and power generation purposes, while the waste heat of the evacuated tube collectors is then exploited for heating. The proposed system is comprehensively analyzed from various standpoints i.e., energy, exergy, and exergoeconomic. Afterward, the multi-objective genetic algorithm optimization – as a suitable tool – is applied to the system to extract a trade-off between the competing objective functions. The results showed that R124 results in better exergetic efficiency compared to other refrigerants. The parametric study outcomes indicate that with increasing the collector area, total product cost reduces but the total cost rate increases dramatically. The results also show that input parameters can directly affect the exergetic sustainability index to be cautiously designed. The optimization results show that the system’s maximum exergy efficiency is 10.06% while the minimum total cost rate is obtained as 0.4835 $/h. Further, the decision factors’ scatter plots show that LiBr mass fraction should be around 25% for optimal operation. Overall, the proposed system can be employed for cooling, heating, and power generation as a potential cycle.

PurposeThe concept of e-learning is essential in employee education since it provides different ways to develop employees' knowledge, skills and attitudes using modern technologies. E-learning has been overgrowing in employee education because learning can be held anytime and anywhere. In order to succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address the factors affecting its success. This paper aims to test the role of internet of Things (IoT)-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework.Design/methodology/approachE-learning systems receive ever-increasing attention in academia, business and public administration. With the development of e-learning, employee education has also benefited from its capacities in various fields. To succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address its success. The proposing of Information and Communications Technology (ICT)-based technologies such as the IoT, cloud, etc., in e-learning, can help transform education. Therefore, this paper aims to test the role of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework. The research model and the data collected from the questionnaires have been analyzed via Smart PLS 3.2. This study has utilized the SEM to evaluate the causal model's reliability and validity based on measurement. According to the literature in this study, a framework has been proposed that examines the impact of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on employees' learning programs' success.FindingsThe results have shown that IoT-based systems, cloud-based services, virtual classes and evaluation tools are four significant factors affecting attitude, content management and creativity. The results have also shown that attitude, content management and creativity are three significant factors affecting employees' learning programs' success. The factors above are considered critical in explaining the success of employees' e-learning programs, but, as far as we know, there has been no study in which all these factors were demonstrated together.Practical implicationsFrom a practical viewpoint, the statistical outcomes support the important role of the following factors: IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity. Henceforth, aspects relating to these factors got the attention of any organization to develop e-learning processes.Originality/valueThis research will contribute to the literature related to employees' e-learning programs' success by integrating all the mentioned variables. As far as we know, it is the first study to test these variables in Iran.

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is perhaps the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 milliseconds and the overall detection test time for 3100 images is 2.721 seconds.

Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster, sorting all within header, and selecting an active one (more power header) for signal routing. While sensor cover topology demonstrates network monitoring capability, connection topology should remain as a requirement for the successful delivery of information including queries, data collected, and control messages. How to build an optimized coating topology while remaining efficient and low-cost connection is not well understood and needs further research. Power control and power management are two different types of topology controllers. Also in our study, we examine network lifetime, compared to other schemas time of death of the first node and the last node, and found that network lifetime was increased. Finally, a topology control method for extending network lifetime is presented.

The rapid developments in the mobile application context illuminate the demand for more resources and processing power at Smart Mobile Devices (SMDs). Mobile Cloud Computing (MCC) enables the SMDs to offload their workloads on the remote cloud servers and benefit from the MCC’s extensive resources to deal with this issue. To this end, numerous offloading schemes are provided in the literature to enhance the SMD's efficiency by offloading their workloads on the nearby cloudlets or remote cloud computing resources. This article puts forward a comprehensive survey and taxonomy of the offloading approaches designed and proposed for MCCs. It first classifies them based on the algorithms which have been used for making the offloading decisions. Then, in each category, it illuminates how the offloading decisions are made to improve application performance and mobile devices' energy efficiency, regarding offloading factors such as deadlines, costs, etc. The evaluation metrics, simulator, offloading type, and architecture of the studied schemes are compared and illuminated in each category. Furthermore, regarding the various properties of the studied offloading methods, the offloading domain's leading issues and challenges are discussed. Lastly, the concluding points are provided, and directions for the subsequent studies in the offloading context are specified.

Roaming is when the mobile user goes out of his/her home agent network coverage and loses its signal. Loss of coverage and signals may be limited to a remote area or may occur when mobile user leaves the country and moves to a country where his/her mobile carrier network is not available. In this case, the mobile device is in roaming mode. In this mode, mobile user through connection to a Foreign Agent can still use its home agent services if his/her authentication be successful. In such situations, the authentication mechanism plays a key and important role, where the mobile user often needs to integrate and secure roaming service over multiple foreign agents. Designing a secure mechanism in Global Mobility Network (GLOMONET) is a difficult and complex task due to the computational and processing limitations of most mobile devices, as well as the wireless nature of communication environment. Unfortunately, most of the authentication schemes that have been proposed so far to meet this goal have failed to achieve their goal. In this line, Shashidhara et al. recently reported security vulnerabilities of Xu et al.’s mobile authentication scheme, and also presented an amended version of it. This paper shows that this proposed scheme has security flaws against impersonation, traceability, forward secrecy contradiction, and stolen smart card attacks, which implies that this protocol may not be a proper choice to be used on GLOMONET. On the other hand, we propose AMAPG, as a cost-efficient remedy version of the protocol which provides desired security against various attacks and also prove its security using BAN logic. We also evaluate AMAPG’s security using Scyther as a widely used formal tool to evaluate the security correctness of the cryptographic protocols.

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

Fall event has become a critical health problem among elderly people. We propose a fall detection system that analyzes real-time streaming data from the Internet of Things (IoT) to detect irregular patterns related to fall. We train a deep neural network model using accelerometer data from an online physical activity monitoring dataset named, MobiAct. An IBM Cloud-based IoT data processing framework is used to manage streaming data. About 96.71% of accuracy is achieved in assessing the performance of the proposed model.

The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

Detecting and recognizing marine mammals have found serious attention recently, given the inhomogeneous underwater sound propagation environment. Meanwhile, the emphasis on applying machine learning and swarm-based intelligence algorithms has become stronger. This paper developed an accurate dolphin recognizer and proposed a multi-layer perceptron neural network (MLPNN). Despite many capabilities, MLPNNs suffer from severe built-in defects tackling real-world problems, including convergence rate, entrapment in local minima, and sensitivity to initialization. Therefore, the chimp optimization algorithm (ChOA) is first utilized to optimize the MLPNN parameters. Furthermore, the modified-chimp concept is introduced and applied to improve ChOA efficiency. Finally, a common benchmark dataset is used, followed by developing an experimental dolphin vocalization dataset to evaluate the performance of the designed model. The results are verified by a comparative study with Slime Mould Algorithm (SMA), Harris Hawks Optimization (HHO), Henry Gas Solubility Optimization (HGSO), and Kalman Filter (KF) approaches, as well as classic ChOA, based on recognition accuracy, convergence speed, and entrapment in local minima. The simulation results indicated that the weighted-chimp optimizer and the proposed recognizer outperform other benchmark recognition methods significantly.

The increasing number of security attacks have inspired researchers to employ various classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection systems (IDSs). This paper presents a comprehensive study and investigation of the SVM-based intrusion detection and feature selection systems proposed in the literature. It first presents the essential concepts and background knowledge about security attacks, IDS, and SVM classifiers. It then provides a taxonomy of the SVM-based IDS schemes and describes how they have adapted numerous types of SVM classifiers in detecting various types of anomalies and intrusions. Moreover, it discusses the main contributions of the investigated schemes and highlights the algorithms and techniques combined with the SVM to enhance its detection rate and accuracy. Finally, different properties and limitations of the SVM-based IDS schemes are discussed.