Image Super-Resolution (ISR) is employed to generate high-resolution images from low-resolution inputs. However, most current techniques for ISR encounter important challenges such as: (i) the assumption of sufficient training data availability, and (ii) the presumption that target image regions are complete without missing data. To address these practically important challenges, this study applies a lightweight approach termed Fuzzy Rough Feature Selection-based ANFIS Interpolation for ISR, especially on Martian imagery. Feature extraction algorithms are first applied to capture potentially significant features, and population-based search mechanisms are then utilised to perform effective feature selection (via extending the popular fuzzy-rough feature selection mechanism). The selected feature set is subsequently fed into an ANFIS interpolation model to perform the ISR task. Particularly, to handle the issue of sparse and incomplete data in dealing with Mars images, two adjacent ANFIS models are trained on nearby regions with sufficient data, positioning the model for the sparse region in between. Experimental studies conducted on Martian image datasets under both sufficient and sparse data conditions validate the effectiveness of the proposed approach, in overcoming the specific challenges faced by the task of ISR in extraterrestrial imaging scenarios.

Image Super-Resolution (ISR) is utilised to generate a high-resolution image from a low-resolution one. However, most current techniques for ISR confront three main constraints: i) the assumption that there is sufficient data available for training, ii) the presumption that areas of the images concerned do not involve missing data, and iii) the development of a computationally efficient model that does not compromise performance. In addressing these issues, this study proposes a novel lightweight approach termed Fuzzy Rough Feature Selection-based ANFIS Interpolation (FRFS-ANFISI) for ISR. Popular feature extraction algorithms are employed to extract the potentially significant features from images, and population-based search mechanisms are utilised to implement effective FRFS methods that assist in selecting the most important features among them. Subsequently, the processed data is entered into the ANFIS interpolation model to execute the ISR operation. To tackle the sparse data challenge, two adjacent ANFIS models are trained with sufficient data where appropriate, intending to position the ANFIS model of sparse data in the middle. This enables the two neighbouring ANFIS models to be interpolated to produce the otherwise missing knowledge or rules for the model in between, thereby estimating the corresponding outcomes. Conducted on standard ISR benchmark datasets while considering both sufficient and sparse data scenarios, the experimental studies demonstrate the efficacy of the proposed approach in helping deal with the aforementioned challenges facing ISR.

Image processing is a very broad field containing various areas, including image super-resolution (ISR) which re-represents a low-resolution image as a high-resolution one through a certain means of image transformation. The problem with most of the existing ISR methods is that they are devised for the condition in which sufficient training data is expected to be available. This article proposes a new approach for sparse data-based (rather than sufficient training data-based) ISR, by the use of an ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation technique. Particularly, a set of given image training data is split into various subsets of sufficient and sparse training data subsets. Typical ANFIS training process is applied for those subsets involving sufficient data, and ANFIS interpolation is employed for the rest that contains sparse data only. Inadequate work is available in the current literature for the sparse data-based ISR. Consequently, the implementations of the proposed sparse data-based approach, for both training and testing processes, are compared with the state-of-the-art sufficient data-based ISR methods. This is of course very challenging, but the results of experimental evaluation demonstrate positively about the efficacy of the work presented herein.

The performance of Deep Convolutional Neural Network (CNN) in the field of computer vision and image processing has shown tremendously amazing results. Nevertheless, the internal hidden and deep network architecture of CNN offers a Black Box computational model. Thus, it is infeasible to interpret and explain the computed results directly from such a model, whilst semantic interpretation and explanation are often required for applications such as fault monitoring in a physical plant and medical diagnosis in a human body. To address this challenging problem, various approaches have been proposed in the emerging area of eXplainable AI. Adaptive Network-based Fuzzy Inference System (ANFIS) provides a possible modelling technique that returns transparent and interpretable reasoning results, but it tends to work with data of a dimensionality considerably lower than that workable with CNN. Yet, an integration of these two different approaches may present a meaningful solution to the problem. This paper introduces such a novel framework which enables an interpretable explanation of the CNN outcomes with an ANFIS. This is facilitated by the use of Fuzzy Rough Feature Selection that converts high dimensional data into low dimensional data while minimising information loss. Initial experimental results illustrate the working of this framework.

The Internet of Things (IoT) has been evolving for more than a decade. Technological advancements have increased its popularity, but concerns and risks related to IoT are growing considerably along with the increased number of connected devices. In 2013, a new cryptography-based infrastructure called blockchain emerged with the potential to replace the existing cloud-based infrastructure of IoT through decentralization. In this article, we provide a taxonomy of the challenges in the current IoT infrastructure, and a literature survey with a taxonomy of the issues to expect in the future of the IoT after adopting blockchain as an infrastructure. The two architectures are compared based on their strengths and weaknesses. Then a brief survey of ongoing key research activities in blockchain is presented, which will have considerable impact on overcoming the challenges encountered in the applicability of blockchain in IoT. Finally, considering the challenges and issues in both infrastructures and the latest research activities, we propose a high-level hybrid IoT approach that uses the cloud, edge/fog, and blockchain together to avoid the limitations of each infrastructure.

The role of Alternaria sp on seed vigor of rapeseed, Brassica napus, was investigated. Seed samples were collected from rapeseed growing regions of Sindh province. The samples were processed for fungal recovery using blotter paper and agar plate methods. Both methods produced a number of parasitic and saprophytic fungi. Among the recovered fungi, Alternaria sp was predominant with 16% average infection. Similarly, effect of seed treatment with Topsin M. was examined in this experiment. It improved seed health significantly over inoculated seeds. The treated seeds showed greater germination (16.15%) with 83.69% more healthy seedling in comparison with inoculated seeds. Root and shoot systems were significantly improved in seedlings from treated seed compared to those from inoculated seeds. The results of this study suggest grading seed samples to reduce seed impurities and seed treatment (particularly with Topsin M) to eliminate seed borne pathogen, especially Alternaria from rapeseed to enhance production by the growers. Key words: Seed health, seed borne fungi, Alternaria, seed treatment.

Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.

In today's age of information technology secure transmission of information is a big challenge. Symmetric and asymmetric cryptosystems are not appropriate for high level of security. Modern hash function based systems are better than traditional systems but the complex algorithms of generating invertible functions are very time consuming. In traditional systems data is being encrypted with the key but still there are possibilities of eavesdrop the key and altered text. Therefore, key must be strong and unpredictable, so a method has been proposed which take the advantage of theory of natural selection. Genetic Algorithms are used to solve many problems by modeling simplified genetic processes and are considered as a class of optimization algorithms. By using Genetic Algorithm the strength of the key is improved that ultimately make the whole algorithm good enough. In the proposed method, data is encrypted by a number of steps. First, a key is generated through random number generator and by applying genetic operations. Next, data is diffused by genetic operators and then logical operators are performed between the diffused data and the key to encrypt the data. Finally, a comparative study has been carried out between our proposed method and two other cryptographic algorithms. It has been observed that the proposed algorithm has better results in terms of the key strength but is less computational efficient than other two.

Jassid (Amrasca devastans) and thrips (Thrips tabaci) have become major pests due to the introduction of Bt cotton. Development of resistance to synthetic insecticides is another menace, keeping in view these facts synthetic insecticide (imidacloprid) and neem derivatives viz., neem oil and neem seed water extract both at 1%, 3% and 5% were assessed against jassid and thrips with two applications. The experiment was conducted from the month of May to October during the year 2016. Eight treatments including untreated control replicated thrice were maintained following RCBD. Two applications of each treatment were made when the pest population reached ETL. Data were recorded after the interval of 1, 3 and 7 days of application. The results revealed that imidacloprid and all the neem derivatives had significantly suppressed the target pests population compared to control. Imidacloprid found to be more toxic than neem derivatives at any interval against the targeted pests. Among the neem derivatives, neem oil at 5% was most efficient bringing about highest percent mortality of jassid (72.03%) and thrips (68.02%) followed by 5% neem seed water extract and 3% neem oil. Higher yield was recorded in plots treated with imidacloprid (2626.7 kg/ha), neem oil (2551.1 kg/ha) and neem seed water extract (2437.8 kg/ha) at 5% concentrations as compared to that in control (1703.3 kg/ha). The study concluded that neem derivatives can be a good alternative of synthetic insecticides to control sucking pests of cotton.

In this paper a new strategy of multiple dictionary learning is proposed for the problem of super-resolution. A two way clustering mechanism is proposed for classification. Dictionaries are obtained for each cluster by coupled dictionary learning with mapping functions. Clustering of training data is carried out by using two approximate scale invariant features. This is followed by coupled dictionary and mapping learning which further helps in making the sparse representation invariant to resolution blur. This mechanism provides a selective sparse coding over multiple dictionaries. At the reconstruction phase each patch is recovered by selective sparse coding and dictionary learning. Experiment results indicate that the proposed algorithm is on par with existing state-of-the-art algorithms. The proposed algorithm is able to recover directional features more accurately.

Blockchain is the one of leading technology of this time; it has started to revolutionize several fields like, finance, business, industry, smart home, healthcare, social networks, Internet and the Internet of Things. It has many benefits like, decentralized network, robustness, availability, stability, anonymity, auditability and accountability. The applications of Blockchain are emerging, and it is found that most of the work is focused on its engineering implementation. While the theoretical part is very less considered and explored. In this paper we implemented the simulation of mining process in Blockchain based systems using queuing theory. We took the parameters of one of the mature Cryptocurrency, Bitcoin's real data and simulated using M/M/n/L queuing system in JSIMgraph. We have achieved realistic results; and expect that it will open up new research direction in theoretical research of Blockchain based systems.

Network architecture of any real-time system must be robust enough to absorb several network failures and still work smoothly. Smart Grid Network is one of those big networks that should be considered and designed carefully because of its dependencies. There are several hybrid approaches that have been proposed using wireless and wired technologies by involving SDH/SONNET as a backbone network, but all technologies have their own limitations and can’t be utilized due to various factors. In this paper, we propose a fiber optic based Gigabit Ethernet (1000BASE-ZX) network named as Territory Substation Area Network (T-SAN) for smart grid backbone architecture. It is a scalable architecture, with several desired features, like higher coverage, fault tolerance, robustness, reliability, and maximum availability. The use case of sample mapping the T-SAN on the map of People Republic of China proves its strength to become backhaul network of any territory or country, the results of implemented architecture and its protocol for fault detection and recovery reveals the ability of system survival under several random, multiple and simultaneous faults efficiently.

Identification of sources of resistance and their incorporation in crop germplasm is the most effective method for disease management. Therefore, present work has been conducted to find out resistance in thirty two (32) wheat genotypes against leaf rust during wheat season 2015-2016 and 2016-2017. The results of the study revealed significant variation among these genotypes against leaf rust in both seasons. In 2015-16 wheat season genotypes C217 and C228 were free of leaf rust, T11, T12, T16 and T18 were found resistant and T1, T2 and T19 showed moderately resistant reaction. Maximum leaf rust severity of 50S has been noticed on T14. In 2016-17 normal sowing trial, less rust development has been observed on all genotypes due to late appearance of disease. While in late sowing trial, seventeen genotypes have been found susceptible with maximum rust severity of 60S on T20. Twelve are rated moderately susceptible to susceptible. While the genotypes T1and T2 showed moderately resistant -moderately susceptible reaction against the disease in late sowing trial. Among thirty two genotypes, T16 has been found free of leaf rust in both trials. Thus the potential of T1, T2 and T16 as source of resistance against leaf rust can be investigated further. These results can be used in wheat breeding program to incorporate leaf rust resistance in wheat genotypes.

In real-time systems, a fault on any node can lead to big financial and life losses. To overcome that issue several approaches have been proposed but the issue of the robust, reliable and cost-efficient network still persists. In our previous work, we have proposed Recursive Scalable Autonomous Fault Tolerant Ethernet (RSAFE) Scheme for such systems to overcome the limitations. In this paper, we present the implementation of RSAFE Scheme and RSAFE Rerouting Protocol in NS2. The simulation results shows that fault detection and recovery is possible in a specified time using RSAFE and its rerouting protocol.

This Analysis and Design of Algorithm is considered as a compulsory course in the field of Computer Science. It increases the logical and problem solving skills of the students and make their solutions efficient in terms of time and space. These objectives can only be achieved if a student practically implements what he or she has studied throughout the course. But if the contents of this course are merely studied and rarely practiced then the actual goals of the course is not fulfilled. This article will explore the extent of practical implementation of the course of analysis and design of algorithm. Problems faced by the computer science community and major barriers in the field are also enumerated. Finally, some recommendations are made to overcome the obstacles in the practical implementation of analysis and design of algorithms.

Citrus production is subjected to the attack of a number of production threats. Citrus canker, caused by Xanthomonas axonopodis pv citri (Xac) is one of them. In the present study five different isolates of the bacterium (Xac) including Xac1, Xac2, Xac3, Xac4, and Xac5 isolated from grapefruit (Citrus paradesi), lime (Citrus limon), rough lemon (Citrus jambhiri), red blood malta (Citrus sinensis) and kinnow (Citrus reticulata), respectively were used. These isolates were tested to find the pathogenic variability on three test hosts including grapefruit, lime and rough lemon. The results of these isolates on pathogenicity, symptoms development and host susceptibility indicated that the isolate Xac3 showed greater virulence on all test hosts, followed by the isolate Xac4, while the response of other three isolates was intermediate. Among the test hosts, grapefruit gave the highest degree of susceptibility to all the strains, while lime and lemon were ranked second and third, respectively.The results of the study suggest variation in the pathogenic nature of these isolates.

IntroductionBalochistan has not only importance due to its vast fields of valuable natural resources and other minerals but it is situated in the South-west of Pakistan, near Iran and Afghanistan borders, the construction of Gwadar port in Arabian Sea has the possibility of bringing development in the region extraordinarily has increased its importance for Pakistan in regional affairs. Before independence, the British took advantage of this region by utilizing it as a buffer zone. They controlled it through Sardars of Balochistan, and this practice still existed after 1947, but several insurgencies outbreak to create independent Balochistan.(Wolpert, 2000, pp. 2-5). Since then, Pakistani central government has been struggling hard to stop eruption of Baloch insurgency and continue to have territorial integrity and sovereignty and have law and order situation under control in Balochistan. Since 1947, the Baloch launched five revolts against the central government of Pakistan. Nevertheless, due to the regional and global changing strategic, political and economic scenarios, its importance has come into focus. Robert Kaplan argues that energy competition is increasing with reducing collection of new and emerging markets (Kaplan, 2010, pp. 4-5).Current NATO war in Afghanistan also express the importance of Balochistan as it provides the entry point for the military and relief goods going to the fighting zones in Afghanistan. Balochistan proved to be a safe route for these military goods as compared to other routes. It is important to mention that insurgency and other extremists and fundamentalist factors in Balochistan are possible indicators for worries in the near future that needs extra amount of care and treatment. Interests and pressures from regional and global powers and its strategic location along multiple sea and land routes has increased its importance that needs careful attention, because number of issues of regional and global dimension are linked to Pakistan's internal politics. Energy issues have increased the interest of not only South Asian countries but also China, Russia and European nations. Decision for construction of the proposed pipelines to carry oil and gas through Balochistan, have increased its importance in terms of and stability of this region.(UNPO, 2010, March 23).Geostrategic Importance of BalochistanThis part of the paper illustrates the geostrategic importance of Balochistan, such as how it is perfectly situated in Southwest of Pakistan and its close proximity to the energy rich regions, Middle East and Central Asia. This part of the study will show the importance of vast, large Balochistan's valuable and useful energy possessions and by what methods the Gawadar deep-sea port will bring globalization into Pakistan. Furthermore, this paper explores in what ways the Baloch insurgency causes problems for regional peace and and the increasing level of international involvement in Balochistan, the potential for global intervention along with their vested and political agendas can push this strategically important region into chaos. Strategic importance of Balochistan exists by joining together its geographic position, natural resources and the people living in and around Balochistan. The focus is on in what way the area of land of Balochistan positioned it at the crossroads of most important business route from South Asia to Southwest Asia.To survive, the states think about two important principles or ideas that complement each other, interests and security. Interests refer to the values that distinguish that state from any other state. However, refers to the survival. Purpose of security is to get and protect the interests of the state. Since each state would like to protect its interests, the extreme and forceful competition is inevitable. Some, usually the powerful states claim what amounts to a prerogative of the mighty to act against the international law if it is believed to be in conflict with their interests. …

Salinity is one of the major abiotic stress in all over the world they are increased day by day due to the insufficient agriculture practices. Pakistan is mostly arid to semiarid country. Out of 23 million ha are cultivated lands, 6.8 million ha cultivated area are mostly affected by soil and water salinity. Populus deltoides, is the eastern cottonwood native to North America, growing throughout the eastern, central, and southwestern USA. P. deltoides is a tree that growing up to15–50m tall. The study was conducted to explore the salinity tolerance of forest species Eastern cottonwood (Populus deltoids) at Department of Forestry & Range Management, University of Agriculture, Faisalabad. Plant species was exposed to different level of salinity stress by using commercial salt NaCl (EC control, 2, 6 and 12 dSm1) in earthen pots experiment to determine the different growth parameters. Results show that salinity have negative effect on biomass production but in EC 6dSm1 the growth parameters were significantly different from control treatment. However in EC 12dSm1 showed significantly decreased as compare to others treatments. Therefore, it is concluded that Populus deltoides well performed under the EC 6dm1. * Corresponding Author: Zikria Zafar  z.zafarfrw@gmail.com International Journal of Biosciences | IJB | ISSN: 2220-6655 (Print) 2222-5234 (Online) http://www.innspub.net Vol. 13, No. 2, p. 191-197, 2018

Late complications of mesh repair are commonly due to mesh migration and erosion into neighbouring visceri. We report the first case of a mesh repair of a lower midline laprotomy incisional hernia complicated by erosion of the mesh into the bladder which presented as haematuria.