Asian Journal of Research in Computer Science
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Conventional and Improved Inclusion-Exclusion Derivations of Symbolic Expressions for the Reliability of a Multi-State Network
No full textThis paper deals with an emergent variant of the classical problem of computing the probability of the union of n events, or equivalently the expectation of the disjunction (ORing) of n indicator variables for these events, i.e., the probability of this disjunction being equal to one. The variant considered herein deals with multi-valued variables, in which the required probability stands for the reliability of a multi-state delivery network (MSDN), whose binary system success is a two-valued function expressed in terms of multi-valued component successes. The paper discusses a simple method for handling the afore-mentioned problem in terms of a standard example MSDN, whose success is known in minimal form as the disjunction of prime implicants or minimal paths of the pertinent network. This method utilizes the multi-state inclusion-exclusion (MS-IE) principle associated with a multi-state generalization of the idempotency property of the ANDing operation. The method discussed is illustrated with a detailed symbolic example of a real-case study, and it produces a more precise version of the same numerical value that was obtained earlier. The example demonstrates the notorious shortcomings and the extreme inefficiency that the MS-IE method suffers, but, on the positive side, it reveals the way to alternative methods, in which such a shortcoming is (partially) mitigated. A prominent and well known example of these methods is the construction of a multi-state probability-ready expression (MS-PRE). Another candidate method would be to apply the MS-IE principle to the union of fewer (factored or composite) paths that is converted (at minimal cost) to PRE form. A third candidate method, employed herein, is a novel method for combining the MS-PRE and MS-IE concepts together. It confines the use of MS-PRE to ‘shellable’ disjointing of ORed terms, and then applies MS-IE to the resulting partially orthogonalized disjunctive form. This new method makes the most of both MS-PRE and MS-IE, and bypasses the troubles caused by either of them. The method is illustrated successfully in terms of the same real-case problem used with the conventional MS-IE
Constructivist Approach for E-Learning Effectiveness in African Higher Education Contexts
No full textThere is a spurt in online learning worldwide, particularly in Asian and African countries, after the COVID-19 pandemic. Outcomes Based Education (OBE), the new paradigm in Africa, India and elsewhere in the world promotes e-Learning as part of its Lifelong learning attribute. Design of such e-Learning system should incorporate features of autonomous learner’s characteristics (like African higher education students) for their preparedness and progression phases of e-Learning. This is due to the fact that the learning environment and learner characteristics, may differ from place to place and cultures. As per literature, principles of constructivist theory have been successfully adapted for e-Learning. Meta-cognition is a new addition to the OBE paradigm besides cognitive, affective and psychomotor domains, that plays important role in e-Learning. This paper attempts to construct metrics for meta-cognition using constructivist’s learning parameters, for analyzing three phases of e-Learning. Literatures are limited on meta-cognitive studies of e-Learner characteristics. The novelty of the paper is the adaptation of certain principles of ecological system on meta-cognition with literature support. Inductive research with appropriate methodology is applied for studying the effectiveness of e-Learning. Ecological factors in the meta-cognition aspects of the constructivist’s theory have been considered. Two parts are treated by the paper: i. Comparative study between Indian and African scenario; ii. Detailed study of selective African countries on the preparedness and progression phases of e-Learning. Survey methods have been chosen for obtaining feedbacks on scientifically designed questionnaire. Three hypotheses on the meta-cognitive aspects of self-regulation and a null hypothesis for the comparative study, have been constructed. Observations on the three phases of e-Learning have been documented and inferences drawn. Conclusions made out our research study along with the results presented will be of immense use to e-Learning system designers, particularly for the African scenario
A Comprehensive Study of Kernel (Issues and Concepts) in Different Operating Systems
No full textVarious operating systems (OS) with numerous functions and features have appeared over time. As a result, they know how each OS has been implemented guides users\u27 decisions on configuring the OS on their machines. Consequently, a comparative study of different operating systems is needed to provide specifics on the same and variance in novel types of OS to address their flaws. This paper\u27s center of attention is the visual operating system based on the OS features and their limitations and strengths by contrasting iOS, Android, Mac, Windows, and Linux operating systems. Linux, Android, and Windows 10 are more stable, more compatible, and more reliable operating systems. Linux, Android, and Windows are popular enough to become user-friendly, unlike other OSs, and make more application programs. The firewalls in Mac OS X and Windows 10 are built-in. The most popular platforms are Android and Windows, specifically the novelist versions. It is because they are low-cost, dependable, compatible, safe, and easy to use. Furthermore, modern developments in issues resulting from the advent of emerging technology and the growth of the cell phone introduced many features such as high-speed processors, massive memory, multitasking, high-resolution displays, functional telecommunication hardware, and so on
An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models
No full textHidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature
Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review
No full textA brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies
Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review
No full textImage compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image\u27s forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression\u27s main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system\u27s input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously
Application of Artificial Neural Network in Distillation System: A Critical Review of Recent Progress
No full textDistillation is a unit operation with multiple input parameters and multiple output parameters. It is characterized by multiple variables, coupling between input parameters, and non-linear relationship with output parameters. Therefore, it is very difficult to use traditional methods to control and optimize the distillation column. Artificial Neural Network (ANN) uses the interconnection between a large number of neurons to establish the functional relationship between input and output, thereby achieving the approximation of any non-linear mapping. ANN is used for the control and optimization of distillation tower, with short response time, good dynamic performance, strong robustness, and strong ability to adapt to changes in the control environment. This article will mainly introduce the research progress of ANN and its application in the modeling, control and optimization of distillation towers
Fingerprint Intramodal Biometric System Based on ABC Feature Fusion
No full textUnimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system\u27s classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS
Rule-Based Expert System for Assist Physician to Diagnosis of Malaria: XPerMal
No full textNowadays, common diseases like malaria, typhoid and cholera become more dangerous problems for people living in this world. The objective is how it can avoid the queue of patients in hospital. In this article, the author has proposed a model of expert systems using the knowledge of physician and other health professionals. The rule based expert system XPerMal useful for patients infected with common diseases and this system will give an answer as similar to a doctor or medical expert and also this system is very useful in rural areas where we have young medical experts or have no medical expert. The reasoning strategy is a key element in many medical tasks. It is well known that developing countries face a shortage of medical expertise in the medical sciences. Patients also find a huge queue in hospitals. Because of this, they are unable to provide good medical services to their inhabitants. The knowledge is acquired from literature review and human experts in the specific field and is used as a basis for analysis, diagnosis and decision-making. Knowledge is represented by an integrated formalism that combines rules and facts
Video Ads in Digital Marketing and Sales: A Big Data Analytics Using Scrapy Web Crawler Mining Technique
No full textThe survival of the global economy is rooted in the production of goods, rendering of valuable services, and formulation and implementation of favorable trade policies. These goods and services supported by related policies however, must reach prospective customers unblemished in good time, through planned advertisement strategies. Advertisement over the years has evolved from the traditional one-on-one to technology induced ones such as digital marketing and sales. Technological advancement has diversified advertisement into a multi-faceted and dynamic channel with enormous growth and prospects. In this paper, we made a significant effort to identify actual online data to justify why short video (SV) adoption is essential in e-commerce and digital marketing. A total of 23589 datasets were drawn from three global B2C and C2C websites using the scrappy web crawlers to investigate a resilience model in the relationship between SV advertising adoption, quality signals, customer satisfaction, price fairness, and sales in digital marketing. Whereas shop location is vital in traditional shopping, logistics service quality overrides its influence in online shopping settings