Asian Journal of Research in Computer Science
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The Role of Cloud Computing Fashionable the Strategic Growth of Business Enterprises in India
No full textIn this 21st Century technology extent of time and worldwide integration, various narrow and medium enterprises exist adopting cloud calculate for their trade operations. Cloud calculate exist an increasing information in visible form Centre technology in accordance with the becoming more intense traffic connected to the internet fashionable the period of the Internet of Things (IoT). These electronics outwit the defect of conventional servers for speed, scalability and effectiveness. However, skilled exist still narrow enterprises that exist undecided of the appropriate of cloud computing time in military operation fashionable trade movement. Thus, this paper is inscribed to survey the views of person who is very involved in education and learning about the benefits of cloud computing rite of a fashionable trade movement that motivate bureaucracy to legally care for business enterprise. The aims of the study include to ascertain the benefits of cloud to small-scale enterprises in India, to determine the challenges facing cloud users and to unravel the strategies that can improve the strategic growth of cloud users in India enterprises. The researcher used a case study design and a qualitative research approach. The place of the study is Chandigarh University of India and Busy Network company in India
LSB-based Audio Steganographical Framework for Securing Data in Transit
No full textThe benefits that individuals and organizations derive from the digital era comes with its own challenges. Globally, data has become one of the greatest assets for decision making and operational improvements among businesses, government agencies and even individuals. Data on its own and at its source does not make so much contribution to business processes. Data is transmitted from one location to another towards attainment of its goal as a critical resource in decision making. However, data including sensitive or confidential ones are transmitted via public channels such as the Internet. The data so transmitted via the Internet is vulnerable to interception and unauthorized manipulation. This demands that data in transit is protected from the prying eyes of the malicious internet users. One of such strategies for transmitting data via public channels such as the Internet without attracting attention from intruders is steganography. In this paper, the least significant bit algorithm was used with an audio file for hiding data in transit. The algorithm used in this research proves to be one of the simplest ways of securing data using audio steganography. The method employed the LSB technique by using audio files as the stego object for the final implementation in the Java programming language. The experimental results proved to be one of the best methods of implementing steganography. The accuracy of the stego objects shows high quality, and similarity scores with an improved processing time.  
Indian Commodity Market Price Comparative Study of Forecasting Methods - A Case Study on Onion, Potato and Tomato
No full textIn this study several characteristics are taken into account so that the crop price forecast is accurate. Forecasting the price of agriculture commodities based on Volume, diesel price helps the agriculturist and also the agriculture mandi’s in India. We look at onion, tomato, and potato trading in India and present the evaluation of a price forecasting model, and anomaly detection and compared differently Supervised, Unsupervised and Forecasting prediction models. We prefer to use wholesale prices, retail prices, arrival volumes of the agricultural commodities and Diesel prices in India. We also provide an in-depth forecasting analysis of the effect on these retail prices. Our results are encouraging and point towards the likelihood of building pricing models for agricultural commodities and to detect anomalies. These data can then be stored and analyzed. The empirical comparison of the chosen methods on the various data showed that some methods are more suitable than others for this type of problem. In this research, we did a comparative study of Auto ARIMA (Autoregressive Integrated Moving Average), RNN (Recurrent Neural Network), LSTM, VAR (vector autoregressive model), and Random Forest Regression, XGBoost in their ability to predict Retail prices of potatoes, onions and tomatoes
Framework for Prioritizing Contact Tracing and Mass Testing of COVID-19 Using Graph Theory
No full textContact tracing has become one of the most useful tools for fighting the novel Corona Virus (COVID-19) pandemic worldwide. The underlining philosophy of contact tracing is determining people who have been in contact with infected persons and thus isolate them from becoming agents of onward transmission of the virus. Slow tracing of contacts and inconsistent or inaccurate information provided by patients usually leads to the spread of the virus along a trajectory at the healthcare systems\u27 blindside. This has led to the proposal of app-based contact tracing solutions. This paper proposes an SQL-based framework that transforms simple interaction data entries into interaction graphs and applies graph theory to prioritize the contact tracing process. The framework returns nodes or individual IDs together with values called Risk_Points to enable individuals\u27 selection for isolation and treatment. Results on simulated data show that the proposed framework can help slow the virus\u27s rate of transmission
The Literature Review of Blockchain Adoption
No full textBlockchain is an emerging technology that depends on distribution, decentralization, and encryption. Recently, it has gained wide interest in various fields of studies. Several considerable research on blockchain adoption has appeared in international journals across different disciplines, however, a review of literature on the adoption of blockchain remains rare. This study aimed to provide a comprehensive literature review of the current blockchain adoption studies that use individual adoption models or theories. This paper included the papers that are published in the 7 Scopus database from 2017-2021. Studies were analyzed to determine the adoption models or theories that are used, industry, country, methodology, and identify the most important influencing factors that drive the user to accept the adoption of blockchain technology. The results showed that there are still limitations in adoption studies in various fields such as health and education however, the field of supply chain management is one of the areas that received the most attention in the studies. Common studies relied on the Technology Acceptance Model (TAM) model as well as the Unified Theory of Acceptance and Use of Technology (UTAUT). Moreover, perceived ease of use (PEOU), perceived usefulness (PU) are considered the most important factors affecting the intention of users in adopting blockchain technology in different fields. The research findings have implications on blockchain adoption research. In terms of theory, the findings provide a guideline for the other researchers to put their own research in a better context and thus contribute to understanding the relevant blockchain adoption issues that require further investigation
Development of a Crowdsourced-based Mobile Application for Measuring Quality of Internet Service Offered by Mobile Network Operators
No full textWireless mobile internet is migrating towards an integrated system of internet and mobile communication network to fulfill the future of mobile network requirement: ubiquitous communication, where mobile users move freely almost anywhere and have access to the internet, communicate with anyone, anytime with any application using the best service available. This demands rapid progress in mobile communication networks and their internet technologies. This research project on the analysis of the Quality of Service (QoS) provided by Mobile Network Operators (MNOs) in the cellular internet networks. It aims to analyze and address the QoS of the internet network experienced by the user. QoS monitoring and analysis require the processing of large amounts of data and knowledge of which kinds of applications the traffic is generated by. To obtain satisfactory QoS based on internet network analysis, some network metrics must be measured and monitored at a regular time interval. In this research work, the network metric that will be measured for the internet service are signal strength, download speed, upload speed, and network transaction latency. This network metrics data will be collected from the consumer’s mobile device by the use of a crowdsourcing mobile application that is installed on the consumer’s mobile device. The investigation will cover mobile communication network providers in Nigeria. The data collated is then evaluated and analyzed
Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods
No full textAs Grid computing continues to make inroads into different spheres of our lives and multicore computers become ubiquitous, the need to leverage the gains of multicore computers for the scheduling of Grid jobs becomes a necessity. Most Grid schedulers remain sequential in nature and are inadequate in meeting up with the growing data and processing need of the Grid. Also, the leakage of Moore’s dividend continues as most computing platforms still depend on the underlying hardware for increased performance. Leveraging the Grid for the data challenge of the future requires a shift away from the traditional sequential method. This work extends the work of [1] on a quadcore system. A random method was used to group machines and the total processing power of machines in each group was computed, a size proportional to speed method is then used to estimates the size of jobs for allocation to machine groups. The MinMin scheduling algorithm was implemented within the groups to schedule a range of jobs while varying the number of groups and threads. The experiment was executed on a single processor system and on a quadcore system. Significant improvement was achieved using the group method on the quadcore system compared to the ordinary MinMin on the quadcore. We also find significant performance improvement with increasing groups. Thirdly, we find that the MinMin algorithm also gained marginally from the quadcore system meaning that it is also scalable
Data Hiding in Digital Image for Efficient Information Safety Based on Residue Number System
No full textThe mass dispersal of digital communication requires the special measures of safety. The need for safe communication is greater than ever before, with computer networks now managing almost all of our business and personal affairs. Information security has become a major concern in our digital lives. The creation of new transmission technologies forces a specific protection mechanisms strategy particularly in data communication state.
We proposed a steganography method in this paper, which reads the message, converting it into its Residue Number System equivalent using the Chinese Remainder Theorem (CRT), encrypting it using the Rivest Shamir Adleman (RSA) algorithm before embedding it in a digital image using the Least Significant Bit algorithm of steganography and then transmitting it through to the appropriate destination and from which the information required to reconstruct the original message is extracted. These techniques will enhance the ability to hide data and the hiding of ciphers in steganographic image and the implementation of CRT will make the device more efficient and stronger. It reduces complexity problems and improved execution speed and reduced the time taken for processing the encryption and embedding competencies
Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower
No full textFor some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology
Web Server Performance Improvement Using Dynamic Load Balancing Techniques: A Review
No full textToday, web services rapidly increased and are accessed by many users, leading to massive traffic on the Internet. Hence, the web server suffers from this problem, and it becomes challenging to manage the total traffic with growing users. It will be overloaded and show response time and bottleneck, so this massive traffic must be shared among several servers. Therefore, the load balancing technologies and server clusters are potent methods for dealing with server bottlenecks. Load balancing techniques distribute the load among servers in the cluster so that it balances all web servers. The motivation of this paper is to give an overview of the several load balancing techniques used to enhance the efficiency of web servers in terms of response time, throughput, and resource utilization. Different algorithms are addressed by researchers and get good results like the pending job, and IP hash algorithms achieve better performance