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Background: This study focuses on Optimization field of computer science which works well when human beings are unable to solve the problem of finding an efficient path. Methods: The in-depth analysis of the problem has followed by the calculation of the school distances. The detailed route formation has been done so that the distances can be actualized in simulation-based optimization. The optimization is based on Genetic Algorithm and for this purpose a graphical user interface has been developed. Findings: The school monitoring teams were given an optimized path for their route to visit where they can easily save the time and the fuel. Results show that the genetic algorithm has produced an efficient and accurate path for the monitoring team so that the fuel and time can be saved. Improvements/Applications: The current study can be extended by comparing genetic algorithm output to any other optimization algorithms, so that the efficiency and time can be measured. Keywords: Genetic Algorithm, Optimization, School Monitoring

While web-based learning is becoming a notable medium for learners opting for distance and open education, it poses its own challenges in terms of usability and navigation. One of these challenges is disorientation in hypermedia settings, resulting in deviation from learning goals. We provide a novel interpretation of the term disorientation by combining the effects seen in virtual relocation in hyperspace (navigational disorientation) with mental relocation seen in mind wandering or external distractions (mental disorientation) based on their similar characteristics and consequences on learning outcomes. This research uses this distinction to measure and resolve disorientation in web-based learning environments. We implement a system with disorientation module which reveals and resolves disorientation, enabling the study of the impact of different kinds of disorientation on learning of students in an online programming course. Study results reveal that learner performance in a web-based learning environment is negatively affected by disorientation, and that learners have to reorient themselves to their learning goals in situations of both navigational and mental disorientation.

Retinal blood vessels play an imperative role in detection of many ailments, such as cardiovascular diseases, hypertension, and diabetic retinopathy. The automated way of segmenting vessels from retinal images can help in early detection of many diseases. In this paper, we propose a framework based on hybrid feature set and hierarchical classification approach to segment blood vessels from digital retinal images. Firstly, we apply bidirectional histogram equalization on the inverted green channel to enhance the fundus image. Six discriminative feature extraction methods have been employed comprising of local intensities, local binary patterns, histogram of gradients, divergence of vector field, high-order local autocorrelations, and morphological transformation. The selection of feature sets has been carried out by classifying vessel and background pixels using random forests and evaluating the segmentation performance for each category of features. The selected feature sets are then used in conjunction with our proposed hierarchical classification approach to segment the vessels. The proposed framework has been tested on the DRIVE, STARE, and CHASEDB1 which are the benchmark datasets for retinal vessel segmentation methods. The results obtained from the experimental analysis show that the proposed framework can achieve better results than most state-of-the-art methods.

In this research work, an automated 3D face expression generation technique is presented, which is extracted from real life video of face motion. The face expression is extracted from real human face using Covariance Matrix for detecting face marker points and Mahalanobis Distance to calculate the distance of each marker points within frames. The technique of tracking points on face uses markers placed on key positions of face muscles, then by getting its position from all frames of pre-recoded face video using the distance algorithm the movement of each face muscle is detected and measured. The face muscles are marked with particular tracking markers that are detected and tracked by the system. This tracking occurs by using color segmentation using Huffman Transform, where we detect color of points and track the location and distance of each tracker points. The original and translated position values of each marker points are obtained and recorded in text file in vector values. The tracked values will be transferred in a 3D Animation software like MAYA and applied on a pre-Rigged 3D model of Human face. The 3D face will be rigged using joints to emulate the face muscle behavior.

Two folded work presents channel allocation scheme sustaining channel orthogonality and channel spacing to reduce CCI (Co-Channel Interference) and ACI (Adjacent Channel Interference) for inter flow of an intra-flow link. Proposed scheme as a part of radio resource allocation is applied on infrastructure based backhaul of wireless mesh network using directional antennas. The proposed approach is applied separately on 2.4 and 5GHz bands. Interference of connectivity graph is modelled by strongly connected directed graph and greedy algorithms are used for channel allocation. We have used OPNET Modeller suite to simulate network models for this research. The proposed arrangement reduces the channel interference and increases system throughput. In this research, the influence of channel is computed in terms of network throughput and delay.

Corpus play a vital role in building the foundation for processing any language recourse. Sindhi being a very old language, yet still is very limited in computational resources. In this paper, Sindhi corpus construction is discussed using XML tagging, to facilitate Sindhi Natural Language Processing and Machine learning features. A two-chain structure is defined for Sindhi language consisting of metadata and Sindhi source Document tags. These tags are further divided to create a complex XML structure with custom tags to identify each Sindhi document extracted from numerous sources.

This article presents a novel architecture using a hybrid model for developing a Sindhi spellchecker system which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language presents a substantial challenge for developing a Sindhi spellchecker system and generating a similar suggestion list for misspelled words. In order to implement such a system, phonetic-based Sindhi language rules and patterns must be taken into account for increasing the accuracy and efficiency. In this research work, a simple and efficient combinational hybrid system is proposed, using three different algorithms, the Edit Distance algorithm to find the measure of similarity between two Sindhi strings. The phonetic-based SoundEx and ShapeEx algorithms are developed for pattern or glyph matching, generating accurate and an efficient suggestion list for incorrect or misspelled Sindhi words. The proposed system is established with a blend between Phonetic-based SoundEx algorithm and ShapeEx algorithm for pattern or glyph matching, generating accurate and efficient suggestion list for incorrect or misspelled Sindhi words. In this article, a table of phonetically similar-sounding Sindhi characters is presented which are grouped together along with another table containing similar glyph or shape-based character groups. The system has been successfully integrated into a pre-developed Sindhi word processer application. The Sindhi word segmentation methodology and algorithms required for the spellchecker has already been published and so are not discussed in detail in this article.

Web-based learning has been one of the most researched areas for the past two decades. Among different problems encountered in e-learning research, an important phenomenon is disorientation. It is the event which results in learners losing their sense of direction in hyperspace or unknowingly deviating from their learning goal. In this paper we review some e-learning systems and their support for identifying or resolving disorientation. We then present architecture of proposed system with an embedded Disorientation Module (DM). DM consists of sub components namely Sensing Module, Resolution Module and Evaluation Module with subsequent techniques for identifying and resolving various types of disorientation. The paper concludes with suggestions regarding future research directions.

The need for integration is not new but it existed since applications moved from central processors to distributed systems and networks. This need has emerged as disparate Information Systems (IS) that automate business processes which run on different computer platforms and have been based on a diversity of standards, operating systems and computer languages. In healthcare, interoperability is the important because of different information systems (IS) to communicate, interchange data, and use of the exchanged information among systems. Blockchain technology can address the interoperability challenges in healthcare systems; it can help healthcare providers, healthcare stakeholders and researchers to share health data in secure way. In this paper, we have proposed a Blockchain based healthcare systems which can address the interoperability issues in healthcare in secure in transparent way.

An image segmentation algorithm, based on human colour perception, has been designed and implemented. An image in the RGB space is obtained through a conventional frame grabber. It is transformed into a perceptual colour space (HSI) and a histogram is constructed to estimate the size of any required feature. A regular decomposition of the image is then made, in which each node contains statistical information about the colour attributes of the pixels in the corresponding region. The best node is selected as a seed, and a merging process then obtains the boundary of the region. The algorithm has been tested on the identification of fluid in colon images observed through a conventional endoscope. In the intended application the segmentation will be part of a control system, and will enable the instrument to suck fluid out of the human colon automatically. Preliminary results suggest that a fast and accurate segmentation can be obtained using simple preceptual colour criteria. Real time performance could be achieved by implementing the algorithm on a small pyramid architecture.