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Wireless Sensor Networks (WSNs) are set to play an important role in the Internet of Things (IoT). WSNs are deployed for many IoT applications like Smart-Street Lighting, Smart-Grid, etc. Time Synchronization Protocol (TSP) is an important protocol in WSNs and it is used for many of its operations. Most of the existing TSPs for WSNs are simulation-based works, which do not fully prove their effectiveness for WSNs. Further, the Line-of-Sight (LOS) conditions in which the WSN nodes are deployed can significantly affect the performance of these TSPs. However, most of the existing protocols neither talk about the LOS conditions in which these protocols were tested nor prove their effectiveness for different LOS conditions. To address these aspects, a synchronization protocol for cluster-based WSNs called a Simple Hierarchical Algorithm for Time Synchronization (H-SATS) has been proposed in this work and its performance is tested on a densely deployed large-sized WSN testbed in different LOS conditions. Further, H-SATS has been compared with the traditional regression-based method, which is the core synchronization scheme for different synchronization protocols in clustered WSNs. Experiments show that H-SATS outperforms the regression method in terms of synchronization accuracy to a maximum of 26.7% for a 30-node network. [ABSTRACT FROM AUTHOR]

The acoustic resonance spectroscopy is an accurate, precise, inexpensive, and non-destructive method for identification and quantification of materials. The acoustics based inspection methods used for classification of materials in the field of food, security, and healthcare is constrained by expensive instrumentation, complicated transducer coupling, etc. Hence, a simple, inexpensive, and portable system has been devised that acquires data quickly and classifies the materials. It has two piezoelectric transducers glued to both ends of the V-shaped quartz tube, one acting as a transmitter and another as a receiver. The transmitter generates vibration by white noise excitation. The receiver detects the resultant signal after interaction with samples and recorded the acoustic signal with the help of a laptop and software. From analysis of power spectrum of signals acquired from each of the samples, seven resonant peaks were obtained. PCA analysis was carried out by selecting only two principal components as feature vectors for classification. The overall accuracy of the classifiers: LDA and Naive Bayes were 98.91% and 96.83% respectively. The classification accuracy of LDA for distilled water, sugar solution, and salt solution were found to be 100%, 98.5%, and 98.25% respectively, while the accuracy of the Naive Bayes classifier was 94%, 98.5%, and 98% respectively. The results show that the classification accuracy of LDA is better than Naive Bayes classifier. The datasets of the developed simple system show a significant capability in the classification of materials. [ABSTRACT FROM AUTHOR]

The Severe acute respiratory syndrome coronavirus (SARS-CoV) are deadly infectious disease which can easily transmit and causes severe problems in humans. It is known as a coronavirus and referred as a common form of virus that naturally causes upper-respiratory tract illnesses and the symptoms are hard to identify. It is important to recognize the patient and providing them with suitable action with constant intensive care. Healthcare amenities is constructed on fog and big-data based system and it is integrated with cyber-physical system. The role of Cyber physical system in health care domain is to fetch deep insights about the nature of disease and carry the monitoring process with early detection of infected users. The objective is to identify occurrence of SARS at initial stage. In proposed system, resemblance factor is evaluated from the extracted keywords. In order to identify the difference between SARS affected and others, the proposed scheme fetches the inputs from user's displayed in the form of text. It is passed to deep recurrent neural network (RNN) model. It extracts useful information from the raw information given by the user. The J48graft algorithm is used to carry the classification based on the type of infection and symptoms of each user. The data is stored in the bigdata layer (mongoDB) and it detects the infected area by using the geospatial feature in mongo dB. The methodology is framed in the proposed model to prevent the spread of disease to other users. In case of any abnormality the generation of alert process is done instantaneously and directed on user's mobile from fog layer. The final experimental outcome reveals information about the performance of proposed system in terms of Success rate, failure rate, latency and accuracy %. It shows that the proposed algorithm gives high level of accuracy when it is compared with other primitive methods. [ABSTRACT FROM AUTHOR]

This paper presents a new evolutionary approach for reconfiguration of radial systems. The framework applied for optimization is Symbiotic Organism Search Algorithm (SOSA). The algorithm is impressed by the interactive behavior opted by the living organisms for surviving and to propagate in the ecosystem. This concept aims for optimal survivability in the ecosystem involving the harm and benefits received from other organisms. The aim is to find optimal reconfiguration and to reduce the real power loss in the distribution side. This approach is examined on 16-bus and 33-bus systems. The results show a significant reduction of real power loss. The time required for execution is less when compared to other approaches. Based on the results calculated with distribution load flow algorithm the SOSA gives better results in terms of real power loss reduction and it is best suitable for digital automation systems. [ABSTRACT FROM AUTHOR]

Ensemble pruning is usually used to improve classification ability of an ensemble using less number of classifiers, and it is an NP-hard problem. Existing ensemble pruning approaches always find the optimal sub-ensemble using diversity of classifiers or running heuristic search algorithms separately. Diversity and accuracy of classifiers are widely recognized as two important properties of an ensemble. The increase of the diversity of classifiers must lead to the decrease of the average accuracy of the whole classifiers, and vice versa, so there is a tradeoff between diversity and accuracy of classifiers. Finding the tradeoff is the key to a successful ensemble. Heuristic algorithms have good results when it comes to finding the tradeoff, but it is unfeasible to do an exhaustive search. Hence, we propose a Spread Binary Artificial Fish swarm algorithm combined with a Double-fault measure for Ensemble Pruning (SBAFDEP) using a combination of diversity measures and heuristic algorithms. First, the classifiers in an initial pool are pre-pruned using a double-fault measure, which significantly alleviates the computational complexity of ensemble pruning. Second, the final ensemble is efficiently assembled from the retaining classifiers after pre-pruning using the proposed Spread Binary Artificial Fish Swarm Algorithm (SBAFSA). Simulation and experiment results on 25 UCI datasets show that SBAFDEP performs better than other state-of-the-art pruning approaches. It provides a novel research idea for ensemble pruning. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing, a cost-effective and reliable choice to employ IT infrastructure, the cyber-physical systems (CPS) are transforming into loosely coupled cloud and fog CPS. The sensor information from physical processes at CPS is continuously processed by fog computing nodes and is forwarded for advanced data analytics offered as a service from the cloud. The computation offloaded by fog devices are initiated as Virtual Machines (VMs) in the cloud data center. The effective placement of these VMs into minimum Physical Machines (PMs) involves economic and environmental issues. Recent research works signify the use of First-Fit Decreasing (FFD) based heuristic techniques to address this NP-Hard problem as a vector bin-packing problem. In this research work, we present a set of hybrid heuristics and an ensemble heuristic to improve the solution quality. The simulation results show that the proposed heuristics are highly scalable and economical in comparison with the individual heuristic-based approaches. [ABSTRACT FROM AUTHOR]

The importance of the surveillance is increasing every day. Surveillance is monitoring of activities, behavior and other changing information. An intelligent automatic system to detect behavior of the human is very important in public places. For this necessity, a framework is proposed to detect suspicious human behavior as well as tracking of human who is doing some unusual activity such as fighting and threatening actions and also distinguishing the human normal activities from the suspicious behavior. The human activity is recognized by extracting the features using the convolution neural network (CNN) on the extracted optical flow slices and pre-training the activities based on the real-time activities. The obtained learned feature creates a score for each input which is used to predict the type of activity and it is classified using multi-class support vector machine (MSVM). This improved design will provide better surveillance system than existing. Such system can be used in public places like shopping mall, railway station or in a closed environment such as ATM where security is the prime concern. The performance of the system is evaluated, by using different standard datasets having different objects and achieved 95% performance as explained in experimental analysis. [ABSTRACT FROM AUTHOR]

Shared visual cryptography is a method to protect image-based secrets where an image is kept as multiple shares having less computational decoding process. Steganography is a technique to hide secret data in some carrier like-audio, image etc. Steganography technique is categorized into four categories. i) Spatial Domain Technique- Image pixel values are converted into binary and some of the binary values changed to hide secret data. ii) Transform Domain Technique- the message is hidden in cover image and then it is transformed in the frequency domain. iii) Distortion Technique-information is stored by changing the value of the pixel. iv)Visual Cryptography Technique-Image is broken into two or more parts called shares. This article proposes a hybrid visual crypto-steganography approach which exploits the advantages of both approaches to protect image based secret in communication. Most of the visual cryptography is applied on black and white images but the proposed method can be applied directly on color images having three channels. This method does not change the image size. Also an exact replica of original image can be reconstructed therefore this process does not result in image quality degradation. This article proposes novel color image share cryptography where seven shares are generated from one color image (correlated/de-correlated color space). These shares are sent to the receiver and original image is reconstructed using all those shares. Share generation and image reconstruction is based on simple operation like pixel shuffling, reversing binary string of the image information, ratio of pixel intensity values. Row key matrix and column key matrix are generated using random function. Pixel positions are shuffled using these two key matrixes. These seven shares namely Row Key, Column Key, Remainder matrix, Quotient matrix, R ratio matrix, G ratio matrix and B ratio matrix are generated. Then Row key matrix, Column key matrix, Remainder matrix, Quotient matrix and three ratio matrices are hidden into separate cover images by LSB encoding technique and sent over the network. Receiver can reconstruct the image if all shares are available only. The proposed method is applied on standard images in the literature and images captured using standard digital camera. Comparison study with existing methods shows that the proposed method performs better in terms of NIST metrics. The method has many applications in the area of visual cryptography, shared cryptography, image based authentication etc. [ABSTRACT FROM AUTHOR]

Functionality and cosmetics are two concerns for future hand prosthesis development and they both can be improved by a combination with artificial soft materials which can mimic human skin. To bridge the gap between the human and artificial side, it is essential to have a comprehensive understanding of the human skin's biomechanics, especially the fingertip's haptics-related mechanism. Available studies characterise the mechanical behaviour of human fingertip only by deterministic models based on either statistical data analysis or fingertip structure/viscoelasticity analysis. To take the force uncertainty into consideration, this paper proposes a novel probability-based haptics model, which includes two parts: a force prediction model to obtain the most possible contact force according to the indentation depth, and a probabilistic model based on Gaussian distribution to describe the force uncertainty. Experiments were conducted by pressing subjects' index fingertips against a cone-shape probe with the measurement of the contact force and the indentation depth under a wide range of 0∼5 mm. Four types of non-linear regression models and the Gaussian distribution model are applied for model training and validation. Experiment results reveal that the contact force varying with the indentation depth presents the characteristics of non-linearity, dispersion, and individual difference. Model testing results confirm the effectiveness of the haptics model on force prediction and force uncertainty description. An example of its application on a virtual hand of a rehabilitation system is demonstrated. [ABSTRACT FROM AUTHOR]

Context aware recommender system has become an area of rigorous research attributing to incorporate context features, thereby increases accuracy while making recommendations. Most of the researches have proved neighborhood based collaborative filtering to be one of the most efficient mechanisms in recommender systems because of its simplicity, intuitiveness and wide usage in commercial domains. However, the basic challenges observed in this area include sparsity of data, scalability and utilization of contexts effectively. In this study, a novel framework is proposed to generate recommendations independently of the count and type of context dimensions, hence pertinent for real life recommender systems. In the framework, we have used k-prototype clustering technique to group contextually similar users to get a reduced and effective set. Additionally, particle swarm optimization technique is applied on the closest cluster to find the contribution of different context features to control data sparsity problem. Also, the proposed framework employs an improved similarity measure which considers contextual condition of the user. The results came from the series of experiments using two context enriched datasets showcasing that the proposed framework increases the accuracy of recommendations over other techniques from the same domain without consuming extra cost in terms of time. [ABSTRACT FROM AUTHOR]

The Distributed Generation (DG) systems are highly useful in recent days for increasing the penetration of renewable energy, in which the design of grid connected inverters is one of the demanding and challenging task. For this reason, different controller strategies are developed in the traditional works for controlling the inverters with increased efficiency. But, it has the major limitations of increased computational complexity, steady state error and reduced compensation capability. To solve these issues, this research work aims to design a new controller by implementing a novel Monkey King Evolution Algorithm (MKEA) for grid connected converters. The motive of this work is to increase the overall effectiveness of the power system by controlling the inverter without affecting its output. Also, it aims to provide a secure and convenient controller for the power converters. Here, the information that is obtained from the system which includes real power, distorted power due to load, reactive power of load, and apparent power of inverter are taken as the input. Later, the four numbers of monkeys are initialized, which evaluates the best solution based on these parameters. Sequentially, the monkey king obtains the best solutions from the monkeys, using which the most suitable and best solution for taking the decision is selected. Based on this, the reference current is generated by performing the voltage regulation, and abc to dq0 transformation processes. During simulation, the efficiency of the controller is analyzed by using the measures of phase voltage, phase current, active power, reactive power, apparent power, grid voltage, and output voltage. The Total Harmonic Distortion (THD) is effectively reduced by using the MKEA based controller design. Extensive simulation and experimental results are presented to validate the effectiveness of the proposed controller and control strategy. [ABSTRACT FROM AUTHOR]

A cognitive radio (CR) can be programmed and configured dynamically to use best wireless channels. Such a radio automatically detects available channels in wireless spectrum, and then accordingly changes its transmission. The CR system consists of primary user or licensed user and secondary user or unlicensed user. The security attacks such as active attack and passive attack are identified between primary user and secondary user and packet loss occurs during packet transmission. The security problem occurring while transmission of signal between primary user and secondary user is rectified by using a hybrid RSA (Riverest, Shaimer and Adleman) and HMAC (Hash Message Authentication Code) algorithms where former is used for key generation and latter is used for tag generation which is sent along with signal. Additionally packet loss incurred in system incurs is reduced with aid of Markov Chain Model during transmission. The comparison results provided showefficiency of the proposed algorithm in cognitive radio system in terms of parameters such as throughput, encryption time, decryption time, Packet Delivery Ratio and energy consumption. [ABSTRACT FROM AUTHOR]

Cryptography is the most peculiar way to secure data and most of the encryption algorithms are mainly used for textual data and not suitable for transmission data such as images. It is seen that the generation of secure key in Image cryptography has been a challenging task in the way of providing secured key generation for the transmitted data. In order to aid secured key generation in this context, an optimized secret key generation based on Chebyshev polynomial with Adaptive Firefly (FF) optimization technique is proposed. The optimized key is utilized with process of shuffling, diffusion, and swapping to get a better encrypted image. At the receiver end, reverse process is applied with optimized key to retrieve the original input image. The efficiency of our proposed method is assessed by the exhaustive experimental study. The results show that the proposed methodology provided correlation coefficient of 0.21, Number of Pixels Change Rate (NPCR) of 0.996, Unified Average Changing Intensity (UACI) of 0.3346 and Information Entropy of 7.995 as compared with the existing methods. [ABSTRACT FROM AUTHOR]

Object detection is a technologically challenging issue, which is useful for safety in outdoor environments, where this object, frequently, represents an obstacle that must be avoided. Although several object detection methods have been developed in recent years, they usually tend to produce poor results in outdoor environments, being mainly affected by sunlight, light intensity, shadows, and limited computational resources. This open problem is the main motivation for exploring the challenge of developing low-cost object detection solutions, with the characteristic of being easily adaptable and having low power requirements, such as the ones needed in on-board obstacle detection systems in automobiles. In this work, we present a trade-off analysis of several architectures using an FPGA-based design that implements ANNs (FPGA-ANN) for outdoor obstacle detection, focused in road safety. The analyzed FPGA-ANN architectures merge outdoor data gathered by a Kinect sensor, images and infrared data, to construct an outdoor environment model for object detection, which allows to detect if there is an obstacle in the near surroundings of a vehicle. [ABSTRACT FROM AUTHOR]

Navigation of multiple robots is a challenging task, particularly for many robots, since individual gains may more often than not adversely affect global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace without colliding amongst themselves. Two solutions for coordination namely Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) have been employed and the efficacy of cooperation strategies have been compared with their non-cooperative counterparts as well as with the fundamental potential field method (PFM). Proposed coordination schemes are verified through simulations. A total of 100 scenarios are considered varying the number of robots (8, 12, 16 and 20). The obtained results show the efficacy of the proposed schemes. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) aim at reducing the risks associated with the transportation system as road accidents are becoming one of the primary causes of death in developing countries. Monitoring of driver behavior is one of the key areas of ITS and assists in vehicle safety systems. It has gained importance in order to reduce traffic accidents and ensure the safety of all the road users, from the drivers to the pedestrians. In this work, we present a context-aware system that considers the vehicle, driver and the environment for driver behavior classification as a safe or fatigue or unsafe driver (representing any other unsafe driving behavior like a drunk driver, reckless driver etc.) using a Dynamic Bayesian Network (DBN). We have designed a questionnaire to obtain the influencing factors that decide safe, unsafe and fatigue driving behavior. The collected data has been analyzed using Statistical Package for Social Sciences (SPSS). It has been observed that several techniques in the past have been proposed for driver behavior classification or detection; which either use specialized sensors or hardware devices, inbuilt smartphone sensors (like a gyroscope, accelerometer, magnetometer and GPS etc.), complex sensor fusion algorithms and techniques to detect driver behavior. The novelty of our work lies in designing and developing a context-aware system based on Android smartphone; that considers the complete driving context (driver, vehicle and surrounding environment) and classifies the driver behavior using a DBN. In order to identify driver fatigue, results from the designed questionnaire and previous research studies have been used without the need for special hardware devices. A DBN that combines all the contextual information has been created using GeNIe Modeler. Learning of DBN has been carried out using the Expec-tation–Maximization (EM) algorithm. The real-time data for DBN learning and testing has been collected on Chandigarh-Patiala National Highway, India using an Android smartphone. The proposed system yields an overall classification accuracy of 80–83%.The focus of this paper is to develop a cost-effective context-aware driver behavior classification system, to promote ITS in developing countries. [ABSTRACT FROM AUTHOR]

Recently, sentiment analysis has become a focus domain in artificial intelligence owing to the massive text reviews of modern networks. The fast increase of the domain has led to the spring up of assorted sub-areas, researchers are also focusing on subareas at various levels. This paper focuses on the key subtask in sentiment analysis: aspect-based sentiment analysis. Unlike feature-based traditional approaches and long short-term memory network based models, our work combines the strengths of linguistic resources and gating mechanism to propose an effective convolutional neural network based model for aspect-based sentiment analysis. First, the proposed regularizers from the real world linguistic resources can be of benefit to identify the aspect sentiment polarity. Second, under the guidance of the given aspect, the gating mechanism can better control the sentiment features. Last, the basic structure of model is convolutional neural network, which can perform parallel operations well in the training process. Experimental results on SemEval 2014 Restaurant Datasets demonstrate our approach can achieve excellent results on aspect-based sentiment analysis. [ABSTRACT FROM AUTHOR]

Wireless Mobile Ad hoc network is the foundation for the Vehicular Ad hoc Network which plays a pivotal role in disseminative safety information to the general public and can be used in commercial applications. Challenging issue in VANET is routing of data, due to swift mobility of vehicles and the network topology which changes with time and speed. Mobile Ad hoc Networks (MANETs) routing protocols cannot fully solve the unique characteristics in vehicular networks. Vehicular Ad hoc Networks system enables the intercommunication between the vehicles by allowing them exchange the traffic data or information. Such kind of exchange of information may create privacy apprehension since the vehicle-generated information can contain much confidential information of the vehicle and its driver. Vehicular networks throw a plenty of unique challenges. The traffic information in the network may greatly affect the traffic decisions. Traffic View is a mechanism that can be routed with a vehicle as the future aspect. In this paper, Energy Aware Methodical Data Forwarding (EAMDF) Mechanism in Vehicular Ad hoc Networks is proposed, Information about the node is collected which is situated at the edge of radio range of the sender node (because of its proximity to the sender and as the information has to move in line with the destination route) and then the packet is transmitted by using the trustworthy greedy position based routing approach through that node. The key aspect of EAMDF mechanism is to prolong the energy of the nodes as well as increasing the packet delivery ratio. The results show that the through put is increased by 50%, packet delivery ratio is increased by 12.5% and also energy is prolonged in the network lifetime compared to other algorithms. [ABSTRACT FROM AUTHOR]

Cyber-physical Social Network (CPSN) becomes an essential component of daily life. In recent years, CPSN has dragged millions of users to convey their social opinions. It is highly desirable to mine influential features from such diverse data to make a prediction on users' Point of Interest (POI). Notably, social ties of the user in a specific location have a tendency to share similar opinions. Thus with the appearance of social links, the location based recommendations become popular to acquire reliable POI recommendations. Collaborative Filtering Recommender System (CFRS) able to discover reliable POI recommendations for the target user based on Location-based CPSN. To enhance the performance of CFRS, a clustering ensemble model is proposed in this article. Four different swarm intelligent based cluster optimization algorithms were utilized to generate finite clusters. The experiment is conducted on two real-time social network dataset to exhibit the performance of the proposed CE-CFRS. The result shows that the clustering ensemble model outperforms a single clustering model in terms of assessment metrics. [ABSTRACT FROM AUTHOR]

Predicting the peak time load among data center and distributing the load will minimize the usage of the power consumption and also will minimize the carbon emission from data center. Reducing the carbon emission by lessening the energy consumption in a data center will impact on environment which will lead to a reduced carbon footprint. The proposed Water Shower Model (WSM) with Circular Peak Time Services (CPTS) has reduced the execution time to 10 ms comparing with Round Robin Algorithm. The load is shared among the data centers by predicting the type of request by the user as Read Only Request (ROR) or Read Write Request (RWR). The ROR will assign the load to an optimized Container and the RWR will assign the load to a Virtual Machine. CPTS is a proposed model used to measure the carbon emission right from the idle state of the server in a datacenter and till it reaches the peak time of the load and vice versa. The advantage of existing Dynamic Voltage Frequency Scaling (DVFS) techniques is used in the proposed model to optimize the resource allotment and adjust the power and speed in computing devices which allocates only the required minimal amount of power for performing a task. [ABSTRACT FROM AUTHOR]

The rapid developments of computation, communication and control contribute to the generation of cyber physical systems (CPS). For full-time urban surveillance or military reconnaissance in complex environments, infrared and visible imaging sensors typically need to be integrated into the CPS. Furthermore, an effective and stable image fusion algorithm is important for CPS to provide images with rich information. Therefore, an image fusion algorithm for CPS is introduced in this paper. Compared with traditional multi-scale and multi-direction decomposition based algorithms, a more efficient MSMD based algorithm is proposed. Firstly, base layers reserved edges and detailed layers are obtained by multi-scale decomposition. Secondly, multi-direction decomposition is employed to base layers rather than detailed layers in traditional method. Then, serials of detailed layers and multi-directional base layers are obtained by choosing the max value based on patch. After the inverse transformation of multi-direction decomposition is conducted for multi-directional fused base layers, the reconstruction result is obtained via superposition of fused base and detail layers. Experiments prove that our algorithm outperforms the art-of-state. [ABSTRACT FROM AUTHOR]

Even though, cloud computing reduces the operating cost by enabling adaptation of virtual machines, it has suffered in selection of optimal virtual machine due to shortage of resource or resource wastage, sudden changes in requirement so it requires optimal resource allocation. Resource allocation is the process of providing services and storage space to the particular task requested by the users. This is one of the important challenges in cloud computing environment and has variant level of issues like scheduling task, computational performance, reallocation, response time and cost efficiency. In this research work we introduce a three-phase scheduling method based on memory, energy and QOS in order to overcome the above issues which also yield low energy consumption, maximum storage and the high level Quality of Service (QoS). Biggest Memory First and Biggest Access First is introduced with NUMA scheduler and cache scheduler for memory scheduling and the optimal VM resulting from the three phases of scheduling is determined by Grey Wolf Optimization (GWO) algorithm. To carry the security level of optimized VMs, Streamline Security and Introspection security analysis are exhausted for detecting the malware VMs which results the secured and efficient VMs for further resource allocation. Our proposed methodology is implemented using the Cloud Sim tool and the experimental result shows the efficiency of our proposed method in terms of security, time consumption, and cost. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) can be attributed to the sudden rise in miniature electronic devices, as well as their computing power and ability to make interconnections. These devices exchange large volumes of confidential information from diverse locations. Similar to the Internet, the IoT has also encountered various issues with information security. Due to limited computing and energy resources in the field of IoT, it is necessary to develop a scheme to ensure feasible and more effective concealment and security properties. This paper proposes a unique methodology that captures an image using IoT sensors, which are subjected to lighter cryptographic operations for conversion into a cipher image, and is then sent to a home server. At the home server, a combined cryptography and steganography approach is employed to conceal the cipher image in a cover image, camouflaging the presence of the secret image, which is then sent to the IoT-Cloud server for storage. During the embedding process, QR decomposition is performed on the RIWT transformed secret image and RIWT - DCT transformed cover image. Modification performed on the R matrix of QR decomposition does not affect the structural properties of the cover image. A block selection algorithm is used to select optimal blocks with high contrast areas to embed the secret image. The experimental results indicate that our scheme enhances imperceptibility, robustness, and resistance to steganalysis attacks. [ABSTRACT FROM AUTHOR]

Efficient servicing of requests in cloud environment has become need of the hour. Cloud services work based on zones in various locations and multiple service requests may be simultaneously considered as a batch and allocated to various zones. Experience-based Efficient Scheduling or EXES focuses on achieving minimum possible waiting time for a batch of requests, under the constraint that overall allocation cost should be less than or equal to a budget limit. Migration of tasks is also possible to balance loads if budget permits and we gain in energy. For each task in a batch and all available zones, a priority value is computed based on previous interaction experience of the zone and the site that generated this task. The zone that produces highest priority for a task, is allocated the task. An SDN controller is in charge of the entire process of priority computation and assigning tasks to zones. Priority is given to requests generating from sites that consumed lesser execution time compared to other sites that have generated requests in request queue of the zone. To the best of authors' knowledge, no existing scheduling scheme in cloud has considered batch processing based on service process experience of zones. [ABSTRACT FROM AUTHOR]

With the advent of IoT, cloud/fog based healthcare systems have become a growing trend in modern healthcare systems. These systems comprise of smart sensors, which on integration with medical devices, generate heterogeneous medical big data that can be used in diagnosis of various diseases. However, there is a continuous flow of large quantity of data in such a systems, due to which it may face many difficulties. Among various pre-requisites for proper functioning of these systems, lifetime is a vital factor. Keeping in view these aspects, the use of new hybrid whale-PSO algorithm (HWPSO) in clustering has been proposed for prolonging the network lifetime by preserving the power of network edge devices. In addition to this, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been taken into account in the selection of cluster leader. The cluster leader is responsible for managing intra-cluster and inter-cluster data communication. [ABSTRACT FROM AUTHOR]

The trustworthiness of consumer evaluation is an important prerequisite for reference to make a decision. Hence, a trust evaluator must recognize biased information (referred to as false recommendation), and do so dynamically. Drawing on the sociological concept of trust fusion, a new trust evaluating model is proposed, one built upon (i) Bayesian updating of the trust evaluation with each transaction, and (ii) the identification and correction of purposefully misleading evaluations according to improved evidence theory. Simulations show that the algorithm's trust value increases slowly with successful transactions, but drops rapidly with a failed transaction, capturing the notion that trust is hard to establish, yet easy to destroy. Further simulations demonstrate the model has good robustness and error tolerance of trust evaluation against false recommendations at varying levels of deception. The algorithm effectively and robustly compensates for deception. [ABSTRACT FROM AUTHOR]

The demand for electricity is increasing very rapidly due to the vast development in industrialization. Generally, at present, for electric power generation, Renewable Energy Sources (RES) are considered as a better alternative option than conventional energy sources. Among the various RES, Solar and wind energy are available in abundantly and hence they can be recognized as a reliable source of power generation. More over this type of Hybrid solar and wind energy systems can be used for rural electrification and modernization of remote area. However, there will be problem of power quality issues such as harmonics, sag etc., hence, this work proposed a novel methodology to improve the power quality of the grid system interfaced with hybrid wind-solar system. In this proposed methodology, shunt active power filter with fuzzy logic based control strategy is introduced to minimize the harmonics present in the system. The proposed topology is validated through dynamic simulation using the MATLAB/Simulink Power System Toolbox. Simulation results demonstrate that the proposed system injects power into the grid from hybrid system with harmonic mitigation. This approach also eliminates the need of additional power conditioning equipment for the improvement of power quality. [ABSTRACT FROM AUTHOR]

Guaranteeing the reliability of cyber-physical systems (CPS) requires analog integrated circuits for correct functioning. Analog integrated circuits capture the continuous signal and amplify the signal for further processing in CPS applications. This paper presents the hybrid swarm intelligence based approach for determining the optimal transistors sizes and bias current values of CMOS differential amplifier and an operational amplifier. We proposed the simplex search based global optimization method called a hybrid grey wolf optimization (GWO) for solving amplifiers circuit sizing problems. Simplex and GWO techniques were combined to improve the local search capabilities of the optimization method. Our main aim is to optimize the transistor size and bias current values using hybrid GWO algorithm for an optimal design of the CMOS amplifiers. CMOS 180 nm technology was utilized to finding the circuit performance using proposed optimization approach. Simulation result shows that the proposed method provides the better result for circuit performance parameters such as DC gain, phase margin, unity gain bandwidth and power dissipation. [ABSTRACT FROM AUTHOR]

Brain tumor image segmentation is process of locating the interesting area in terms of objects, like tumor and extracting it for the further process of the image and getting the boundaries of the image for analysis. The bio-medical brain tumor image segmentation is a great challenging field for the today world active researchers with the standardized image datasets and various metrics used for evaluating and comparing the performance of the new algorithm with existing segmentation algorithms. In recent development, these problems are addressed using various image manipulation tools and rapid growth of computer hardware enhancement. Image segmentation was done in three ways: (1) Manual-based (2) Semi-automated-based (3) Fully automated-based. But still be a short of research in the field of brain tumor segmentation and accurate identification of tumor cells. To overcome all the above-mentioned challenges and complexity of the brain tumor segmentation, it need to understand the pre-processing of the image like, registering the image, correction of bias in image, and non-brain tissue removal. In this paper, we propose a new methodology for segmenting the brain tumor from the affected brain image in a significantly efficient way by using deep learning method. [ABSTRACT FROM AUTHOR]

In recent years, a large number of spectrum mechanisms have been proposed, but these mechanisms ignore the security issues that arise during the design of the mechanism. In this paper, two secure models for sealed-bid spectrum auction are given based on Wang's generic spectrum auction mechanism. One is the basic model and another improved model based on the basic model is proposed, which maximizes Social welfare while it is a Privacy-preserving Spectrum auction mechanism with public Verification namely SPSV. The SPSV scheme achieves the properties of maximizing the social welfare but also, by using the double paillier cryptosystem, it is privacy-preserving for bidders' bids without revealing any sensitive information to auctioneer or agent during the entire spectrum auction. Oblivious transfer is applied to ensure the anonymity of bidders. Furthermore, the use of inequality comparison proof also provides the public verification of winner group to verify the comparison relationship between winner groups and losing groups. At last, the performance analysis are given. [ABSTRACT FROM AUTHOR]

Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules. [ABSTRACT FROM AUTHOR]

Rapid web growth and associated applications have proven of colossal importance for recommender systems. In the current digital world, a recommender system aims to acquire high-level prediction-based accuracy. However, many studies have suggested diversity-based recommendations are required for high-level accuracy. Group recommendation systems (GRS) recommend lists of items to a group of users according to their social activities, such as planning for a holiday tour, watching movies, etc. Using GRS, preferences/choices shared by users affected all the available aggregation with GRS leads to information loss and negatively affects 'diversity.' To handle the problem of 'information loss,' which is caused by aggregation, this paper proposes fuzzy-based GRS and argues that communicating such hesitant information will prove beneficial to generating recommendations. To find the valuable suggestions, greater focus must be dedicated to avoiding lack of variety and interest in the complete list of recommendations. In this article, we propose a novel Parallel Computing Group Recommendation System, which quantifies different approaches, chooses the right approach for group recommendation, and quickly generates optimal results. This proposed approach is an ensemble model of parallel ranking and matrix factorization that facilitates a diversified group recommendation list. Experimental evaluation signals that our model achieves higher diversity positively packed with user satisfaction. [ABSTRACT FROM AUTHOR]

In this manuscript a DC non-isolated converter model with high static voltage gain module is presented. The proposed converter has the feature of stable frequency and stable output voltage. It also achieves high voltage conversion, high efficiency, low voltage stress and less switching loss. The voltage tripler technique is implemented in the proposed model. The designed converter model attains high static gain with reduced duty cycle. The proposed single switch converter is controlled by fuzzy-PI controlled technique. The working process of the converter under Continuous Conduction Mode (CCM) is explained. The 30 V input source is boosted up to 400 V. The simulation of the presented converter is done with MATLAB simulink. The hardware prototype is also tested and results are analysed. [ABSTRACT FROM AUTHOR]

3D Cardiac Magnetic Resonance Imaging (MRI) is widely used for the diagnosis of cardiac diseases such as congenital heart defect, left ventricular hypertrophy and left atrium hypertrophy etc. It is one of the noninvasive technique to examine cardiac anatomy. However this technique is semi- automatic, i.e. the images obtained directly from MRI machine have to be segmented manually. This includes the segmentation of chambers and vessels, which is quite complex and requires specialized technical knowledge. Without proper segmentation, it is extremely difficult for medical staff to examine the data. This paper suggest a fully automatic method for cardiac chamber segmentation (Left Atrium and Left Ventricle pair) in 3D cardiac MRI based on artificial intelligence. The proposed method identifies the junction of Left Atrium (LA) and Left Ventricle (LV) using neural networks. The features used for this purpose are based on shape, size and position. Then it uses traditional methods to track and stack the upper and lower slices based on neighborhood. I.e. a 3D model of the segmented LA and LV is reconstructed from the 2D format. This enhanced 3D image model helps in deducing quality information for the diagnosis of various heart diseases. The proposed algorithm shows acceptable performances for all planes of LV and LA. We have achieved 91.57% mean segmentation accuracy. The proposed algorithm is not effected by the thickness of the slices. It is simple and computationally less intensive than existing algorithms. [ABSTRACT FROM AUTHOR]

With the recent developments in digital technologies, the cultural heritage domain has attracted new researchers to drive technological trends for offering interactive and pleasant experience to users. The emergences of location-based services, recommendation systems, and the internet of things have created a smart research space to support cultural heritage visitors. In this paper, we present an intelligent cultural space through the development of proximity detection method and the deployment of smart sense bricks. The presented intelligent cultural space is supported by the CHXplorer-Information Management System to present multimedia content to the users through our CHXplorer mobile decision-support tool. The CHXplorer is designed to enhance the satisfaction and enjoyment of the user visiting the cultural heritage sites, and it is capable of generating personalized recommendations to the active target user. The effectiveness and efficiency of the presented CHXplorer are experimentally evaluated and user study made establishes the users' positive feedback on the system. [ABSTRACT FROM AUTHOR]

In order to accurately assess the threat of air multi-target in the complicated and changeable air combat environment, an assessment method based on improved group generalized intuitionistic fuzzy soft set (I-GGIFSS) is proposed in this paper. Firstly, considering the characteristics of air target and the influence factors of threat assessment, a reasonable threat assessment system is established, and the appropriate assessment index is determined. Secondly, the generalized parameter matrix provided by many experts is introduced into the generalized intuitionistic fuzzy soft set (GIFSS) to form the group generalized intuitionistic fuzzy soft set (GGIFSS) to compensate for the knowledge limitation and assessment error of a single expert in traditional GIFSS. Finally, subjective weight is determined by group AHP (GAHP) and objective weight is determined by intuitionistic fuzzy entropy (IFE), then subjective weight and objective weight are combined based on relative entropy theory to determine reasonable index weight and expert weight, thus I-GGIFSS is obtained. The validity and superiority of I-GGIFSS are verified by the calculation and comparison of an example. [ABSTRACT FROM AUTHOR]

The natural activities and human thinking forms the basis for fuzzy logic which presents based on different application perspectives. The performance of various energy storage systems life time can be improved by utilizing fuzzy logic controllers and back up in a hybrid power system especiallywhile usingrenewable sources. The Load Frequency Controllers (LFC) using classical control techniques are tuned based on trial and error methods.Also, when system complexity increases controller gives slow response, by considering the fuzzy intelligent control these system performances are improved. A soft computing fuzzy technique is employed to maximize the efficiency from solar panel to give maximumpower output.The various applications in power systems relating to energy storage system performance for energy management, controller for controlling the load-frequency in multi-area power system and for solar systems by considering the tracking efficiency which are utilized for synchronization into the grid.The fuzzy logic provides better improvement and efficiency when compared to conventional controllers. These controllers do not have any specific or particular procedure to implement in various applications. A brief review to fuzzy logic controllers (FLC) for energy storage systems LFC and PV solar MPPT is presented. [ABSTRACT FROM AUTHOR]

Edge computing offers potential benefits to applications working in IoT (Internet of Things) and CPS (Cyber Physical Systems) environments by bringing the power of computing proximate to the devices, which demand high computational resources. As computational capabilities are currently untapped in edge devices like the IoT gateway, the computational intensive part of an application like a thread, a module or a task can be offloaded to the edge devices rather than to the cloud by the end devices. In this paper, an approach that employs Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is used to determine the near optimal solution for scheduling offloadable components in an application, with the intent of significantly reducing the execution time of an application and energy consumption of the smart devices. With a new inertial weight equation, an Adaptive Genetic Algorithm – Particle Swarm Optimization (AGA-PSO) algorithm is proposed which uses GA's ability in exploration and PSO's ability in exploitation to make the offloading optimized without violating the deadline constraint of an application. [ABSTRACT FROM AUTHOR]

Authentication based on utilization of fingerprint has become highly popular. Generally, minutiae points information obtained from the fingerprints is stored into the database. Various research works depict that by utilizing minutiae points information, original fingerprints reconstruction is possible. Adversary can obtain the user template through an attack on the database. If minutiae template of a user is compromised, then the adversary can construct original fingerprint of the user. In order to avoid this, it is essential to secure the fingerprint information. To achieve this, a technique called 3-Dimensional Secured Fingerprint Shell has been presented by Ali and Prakash, in this technique they generate a 3-dimensional spiral curve, however, ridge counts between singular and minutiae points are not secure in this technique. In the proposed technique we construct an extremely secure and efficient template for a user, by securing all features of the fingerprint used for template generation. The proposed technique is robust against the effects of rotation as well as translation and meets the essential requirements of an efficient authentication system. We evaluated the proposed technique by utilizing FVC2000, FVC2002, FVC2004, and IIT Kanpur fingerprint databases. The results obtained for the proposed technique are highly encouraging, that shows its robustness. [ABSTRACT FROM AUTHOR]

In this paper, typical application of Cyber Physical System (CPS) has been highlighted for the critical-healthcare data transmission services. Sensors of CPS are providing patient's health information via a communication network to a medical practitioner at some distant place. Needs of development of dependable routing protocol for such type of applications in healthcare are increased day-by-day. This paper proposes a quickest, critical and energy efficient routing for the CPS based healthcare system. Simulations performed to convey the appropriateness of the quality of data transmission system according to the well-defined Service Level Agreement (SLA) formulation. Proposed energy and SLA cooperation in data transmission is beneficial for the tele-operated medical service. A medical practitioner is able to monitor a patient in real-time with the help of proposed dependable and energy efficient data transmission. The results shows that CPS with consideration of different constraints such as energy and SLAs have a severe effect on its performance parameters such as mean number of QSS s-t paths, average hop counts and average energy efficiency. In addition to this comparison with existing algorithms shows the improvement in consideration with all performance components. [ABSTRACT FROM AUTHOR]

Increase in chronic diseases among people gives the health care industry a challenging problem. Healthcare industry is using the Internet-of-Things (IoT) to create systems that monitor patients. The ultimate task of the system is to minimize manual efforts made to recommend food and drugs for chronic patients. However, many uncertain factors are involved with chronic patients, and existing models unable to handle it efficiently often lead to poor results. Medical records gathered from diverse devices, such as mobile and IoT devices that are raw in nature or in different formats cannot be utilized for further analysis. Since patient records grow rapidly, it is difficult for health care systems to manage and control. To overcome these limitations, the proposed system develops a fuzzy ontology-based recommender system using Type-2 fuzzy logic to recommend foods and drugs for chronic (diabetic) patient. Extraction of risk factors for chronic patients is achieved via wearable sensors and IoT-based electronic medical records are linked with linked open data (LOD) to create a knowledge base. Since, patient data sets are huge; cloud services are used to store and retrieve data for further analysis. An experiment is conducted on patient datasets and the results illustrate that the proposed work is efficient for patient data enrichment, risk factor extraction and appropriate medical advice for chronic patients. [ABSTRACT FROM AUTHOR]

Nowadays, the purchase of every product involves a lot of critical thinking. Every buyer goes through a lot of user reviews and rating before finalizing his purchase. They do this to ensure that the product they purchase is of good quality at minimum price possible. It is evident now that online reviews are not that reliable because of fake reviews and review bots. Now you can even pay certain social media groups to give your product a fake good rating. Hence going just with the reviews of some stranger whom you do not know is not intelligent. So we propose a recommendation model based on the Trust Relations (TR) and User Credibility (UC) because it is human nature that a person feels more comfortable when he gets a review from a person he knows on a first name basis. Also, the credibility of the reviewer is an important factor while providing importance to the reviews because every person is different from other and can have different expertise. Our model takes into account the effect of credibility which is not used by any other recommendations models which increases the precision of the results of our model. We also propose the algorithm to calculate the credibility of any node in the network. The results are validated using a dataset and applying our proposed model and traditional average rating model which shows that our model performs better and gives precise results. [ABSTRACT FROM AUTHOR]

Automated metering Infrastructure (AMI) is an integral part of a smart grid. Employing the data collected by the AMI from the consumers to generate accurate electricity consumption forecasts can help the utility in significantly improving the quality of service delivered to the consumer. Design and empirical validation of machine learning based electric energy consumption forecasting systems, is presented in the present study. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Extreme Learning Machines (ELM) based models are designed and evaluated. One of the major aspects of the work is that the proposed consumption forecasting systems are designed as generalized models, i.e. one single model can be used to generate forecasts for any of the consumers considered, as opposed to the conventional technique of generating a separate model for each consumer. The forecasting systems are designed to generate half-hour-ahead and two-hour-ahead electric energy consumption forecasts. The proposed systems are validated on data for 485 Small and Medium Enterprise (SME) consumers in the CER electric energy consumption dataset. Results indicate that the models proposed in the present study result in good consumption forecast accuracy are hence, well suited for generating electric energy consumption forecast models. [ABSTRACT FROM AUTHOR]

Nowadays Electronic communication is an important medium and an inevitable way for official communication. So, the email classification into spam or ham gains a lot of importance. Commonly used approaches are text-based or collaborative methods for spam detection. However, not only choosing the right classifier is very difficult but, handling poison attacks and impersonation attacks are also very important. The proposed model considers a powerful spam filtering technique which includes both social network and email factors in addition to the email data analysis for spam classification. The incoming emails are subjected to header parsing for finding the trust and reputation of senders with respect to the receivers and keyword parsing is applied to find the topic of interest using LDA with Gibbs Sampling method. Optical Character Recognition (OCR) method is applied to find the image spam e-mails. Degree and strength of the connection between the users from the social networks are also considered along with the email data factors for better message classification. Logistic Regression is used to combine all the independent input features to get an effective result. The experimental results and comparisons with the existing models vividly show the significant performance of the proposed classifier. [ABSTRACT FROM AUTHOR]

This paper proposes a prediction system to identify the type of eye diseases like glaucoma and diabetic retinopathy. The proposed system processes the images captured using the fundus camera that is connected to the computer. The acquired fundus images are fed into the proposed prediction system which can be deployed in the cloud, and it identifies the type of disease. This forms a cyber-physical system. Underdeveloped countries which do not have the necessary infrastructure can utilize this service when this system is deployed in the cloud. For identifying these diseases, ophthalmologists extract parameters manually from the fundus image, which is a difficult task. Hence, this research work attempts to develop a system to automate the feature extraction from fundus images and with the extracted features, eye diseases are predicted. From the literature, it is found that many research works were focused on the binary classification of any one disease. In this paper, a novel classification methodology is proposed that helps the experts and clinicians to classify Diabetic Retinopathy, Glaucoma and healthy eye images with more accuracy. The proposed system with high accuracy is designed with the following phases: i) image acquisition, ii) image enhancement, iii) local features extraction using Speeded Up Robust Feature (SURF), iv) Bag of Features/Visual Words (BoF/BoVW) obtained through k-means clustering of local features, and v) classification using Error-Correcting Output Code (ECOC) linear SVM. It is inferred from the results that proposed method of classification using BoVW provided a maximum accuracy of 92% when compared to other state-of-the-art recent literature. [ABSTRACT FROM AUTHOR]

In contrast to conventional preprocessing aided spatial modulation (PSM), which carries partial information using the indexes of receive antennas, we exploit one receive antenna to implicitly convey information and meanwhile harvest energy at the remaining antennas. Based on this, we propose two novel beamforming schemes. The first scheme is to maximize the sum energy harvested by the receiver. And the second scheme is to maximize the minimum receiving power on each antenna except for the antenna that conveys information. A closed form solution and an iterative algorithm are given, respectively. Simulation results demonstrate that proposed two schemes can harvest a certain amount of energy with nearly same achievable rate compared to the benchmark schemes. But the second scheme is superior to the first scheme and PSM scheme in terms of bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]