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Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil's diameter at different light intensities. The proposed framework consistsof two-phasemethodologies. In the first phase, thepupil's diameter is calculatedby applying stimulus (light) in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames. The above measurement is converted into feature space using Kohn and Clynes model-defined parameters. The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal (or when the eye is alive) or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph, video, or 3D mask of the subject in front of the camera. In the second phase, we perform the facial recognition process. Scale-invariant feature transform (SIFT) is used to find the features from the facial images, with each feature having a size of a 128-dimensional vector. These features are scale, rotation, and orientation invariant and are used for recognizing facial images. The brute force matching algorithm is used for matching features of two different images. The threshold value we considered is 0.08 for good matches. To analyze the performance of the framework, we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets, which were used because they contain the videos of the subjects in each sample having three modalities (RGB, IR, Depth). The CASIA-SURF datasets showed an 89.9% Equal Error Rate, while the Replay Attack datasets showed a 92.1% Equal Error Rate. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real‐time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper. [ABSTRACT FROM AUTHOR]

Image texture analysis is a dynamic area of research in computer vision and image processing, with applications ranging from medical image analysis to image segmentation to content-based image retrieval and beyond. "Quinary encoding on mesh patterns (MeQryEP)" is a new approach to extracting texture features for indexing and retrieval of biomedical images, which is implemented in this work. An extension of the previous study, this research investigates the use of local quinary patterns (LQP) on mesh patterns in three different orientations. To encode the gray scale relationship between the central pixel and its surrounding neighbors in a two-dimensional (2D) local region of an image, binary and nonbinary coding, such as local binary patterns (LBP), local ternary patterns (LTP), and LQP, are used, while the proposed strategy uses three selected directions of mesh patterns to encode the gray scale relationship between the surrounding neighbors for a given center pixel in a 2D image. An innovative aspect of the proposed method is that it makes use of mesh image structure quinary pattern features to encode additional spatial structure information, resulting in better retrieval. On three different kinds of benchmark biomedical data sets, analyses have been completed to assess the viability of MeQryEP. LIDC-IDRI-CT and VIA/I–ELCAP-CT are the lung image databases based on computed tomography (CT), while OASIS-MRI is a brain database based on magnetic resonance imaging (MRI). This method outperforms state-of-the-art texture extraction methods, such as LBP, LQEP, LTP, LMeP, LMeTerP, DLTerQEP, LQEQryP, and so on in terms of average retrieval precision (ARP) and average retrieval rate (ARR). [ABSTRACT FROM AUTHOR]

Organic farming is well-known as a traditional farming method which is responsible in producing the hygienic food product. The organic farm is the integration of agricultural production and system management. Organic farming includes the usage of low pesticides, maintains low nitrate leaching in groundwater as well as surface water, uses animal wastes, and minimizes soil erosion. Crop rotation, natural pest control, and barrier nets are the conventional methods of fertilization techniques. Organic farming makes use of recycling resources comparatively to using chemical fertilizers. A natural compost, animal manure with crop rotation implementation will improve the soil quality. Thus, organic foods are highly hygienic without causing any side effects. A few challenges arise during the implementation of organic farms. These challenges are overcome only via the smart approach and planning and coordination of public and government officials. Organic seeds take a long time to grow, and it is an economically high cost to implement this type of farming. Smart transport and supply of organic products to any location is a bit difficult. The proper selection of seed and selection of land with the suitable climate and soil texture that has to be verified for cultivation is the restrains of organic agriculture. Soil moisture content and sufficient water for the crop are attained through the usage of remote sensing in agricultural farms. The food crisis can be managed via the adaptation of remote sensing. The sensor collects information about the soil, water supply, temperature, and other environmental factors to the control unit. Initially, the infected plant with leaves will be isolated as single image as leaf, and each leaf would be diagnosed with various kernel functions including Cauchy, Invmult, and Laplacian kernel. The proposed system uses image processing to capture the images, and the remote sensing sensor generates the information to the Support Vector Machine which classifies the infected plant or part of the plant from the healthy plant based on the features extracted. The proposed Support Vector Machine (SVM) used a remote sensing technique in detecting the compliance discrepancies in an organic farm. [ABSTRACT FROM AUTHOR]

The fog computing models are getting popular as the demand and capacity of data processing is rising for the various applications every year. The fog computing models incorporate the various task scheduling algorithms for the resource selection among the given list of virtual machines (VMs). The task scheduling models are designed around the various task metrics, which include the task length (time), energy, processing cost etc. for the various purposes. The cost oriented scheduling models are primarily built for the customer's perspectives, and saves them a handful amount of money by efficiently assigning the resources for the tasks. In this paper, we have worked upon the multiple task scheduling models based upon the Local Regression (LR), Inter Quartile Range (IQR), Local Regression Robust (LRR), Non-Power Aware (NPA), Median Absolute Deviation (MAD), Dynamic Voltage and Frequency Scheduling (DVFS) and The Static Threshold (THR) methods using the ifogsim simulation designed with the 50 nodes and 50 virtual machines, i.e. 1 virtual machine per node. All of the models have been implemented using the standard input simulation parameters for the purpose of performance assessment in the various domains, specifically in the time domain and effective consumption of energy. The results obtained from the experiments have shown the overall time of 86,400 seconds during the simulation, where the DVFS has been recorded with the 52.98 kWh consumption of energy, which shows the efficient processing in comparison to the 150.68 kWh of energy consumption in the NPA model. Also, there are no SLA violations recorded during both of the simulation, because no VM migration model has been utilized among both of the implemented models, which clearly shows that the VM migrations are the major cause of SLA violation cases. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs). [ABSTRACT FROM AUTHOR]

Fog computing (FC) is becoming popular for connecting real-time Internet of Things (IoT) applications such as driverless cars and health care. Although fog has low latency compared to the cloud, the fog becomes overloaded when serving a large number of IoT devices. FC struggles while distributing the load to the resources that may result in certain fog nodes being overloaded or underloaded. To overcome these issues, an effective load balancing algorithm named Fuzzy Golden Eagle Load Balancing (FGELB) is proposed. The proposed load balancing strategy consists of three stages, namely: assigning priority to the tasks, resource ranking and scheduling, and power management. Initially, the priority is assigned to the incoming task by considering the deadline time, task size, and predefined priority using a fuzzy algorithm. This prioritization of tasks helps to execute the important task without delay. Then the Golden Eagle Optimization algorithm (GEOA) is used to rank and schedule the resources. This ranking and scheduling help the tasks to be executed with suitable resources. Ranked resources execute the tasks in the resource engine, and the power engine manages the power by enabling/disabling the resources based on their necessity. The performance of the approach is tested and it is compared with the results of existing methods in terms of energy consumption, failure rate, computational cost, communication overhead, average turnaround time, and waiting time. The results show that the proposed FGELB approach achieves higher performance than the existing load balancing approaches. • Fuzzy logic algorithm prioritises the task. • GEOA ranks the resources based on user requests. • GEOA also allocate resources to the task. • Power engine monitors real time status of task and performance of resources. • Power engine also manages the power by enabling/disabling the power. [ABSTRACT FROM AUTHOR]

Summary: It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size. [ABSTRACT FROM AUTHOR]