The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

Docker containers usage in the internet of things: a survey

The Internet of Things (IoT) opened the way for enabling many of our everyday objects (things) interact with their environment to collect data, analyze and automating jobs based on specific rules. Within the constraint environment, the requirement of lightweight IoT application are tremendously indeed required to ensure the IoT application can be run efficiently. Docker containers is a promising technology to enable IoT application running smoothly, fast and efficient. In this paper, an introduction to Docker is presented. Then we explore the usage of Docker containers in the IoT application. Finally, we briefly discuss why Docker containers are usage in the IoT application

Data Quality in Big Data: A Review

Abstract The Data Warehousing Institute (TDWI) estimates that data quality problems cost U.S. businesses more than $600 billion a year. The problem with data is that its quality quickly degenerates over time. Experts say 2 percent of records in a customer file become obsolete in one month because customers die, divorce, marry, and move. In addition, data entry errors, system migrations, and changes in source systems, among other things, generate bucket loads of errors. More complex, as organizations fragment into different divisions and units, interpretations of data elements change to meet the local business needs. However, there are several ways that the Company should concern, such as to treat data as a strategic corporate resource; develop a program for managing data quality with a commitment from the top; and hire, train, or outsource experienced data quality professionals to oversee and carry out the program. The Organizations can sustain a commitment to managing data quality over time and adjust monitoring and cleansing processes to changes in the business and underlying systems by using the Commercial data quality tools. Data is a vital resource. Companies that invest proportionally to manage this resource will stand a stronger chance of succeeding in today's competitive global economy than those that squander this critical resource by neglecting to ensure adequate levels of quality. This paper reviews the characteristics of big data quality and the managing processes that are involved in it

RFID-Based Electronic Fare Toll Collection System for Multi-Lane Free Flow - A Case Study towards Malaysia Toll System Improvement

RFID-based Electronic Toll Collection (ETC) system for Multi-lane free flow (MLFF) is a system that enables collection of toll payments electronically using RFID tags, allowing for nonstop toll collection and free-flow of high-speed travelling at toll highway with use of ordinary multilane road segments and aim to eliminate toll plazas and booths. The system allows vehicles with passive RFID tag to emit communication with front-end reading system to uniquely identifying and classifying each vehicle and transfer the toll transaction back to a centralized back office system for revenue collection by deducting from the account of vehicle owner. The RFID-based ETC systems have been implemented in developed and developing countries like Turkey, Taiwan and countries of South America. The review is focused on the RFID-based ETC system architecture that has been implemented by some these countries. The review may help to understand how the overall system works hence it shall provide the essential information in migrating and re-engineering current toll booth ETC lane system to MLFF system and serve as a reference view of the system concept in developing a new RFIDBased ETC system for MLF

Enterprise resources planning implementation success factors of steel industry

In order to survive in a rapidly changing business environment, organizations must improve their business practices and procedures.A steel industry has to undergo various procedures before embark on the production phase.Hence, enterprise resource planning (ERP) can be considered as the most important systems in this type of organization as it serves as the organization business operation backbone handling all the bulky procedures and processes efficiently.The difficulties and high failure rate in implementing ERP systems have been widely discussed in the literature. However, factors affecting ERP implementation are complex and abundant thus should be investigated contextually.The objective of this paper is to explore the key issues that possibly influence ERP systems implementation in one of the enterprise steel industry organizations.Several factors deduced from literature were used to further investigate concerning the relevancy of the factors in the context of the study.The factors were further validated through expert reviews with five ERP consultants using semi-structured interviews.Consequently, seven from eight deduced factors were found to be critical to be considered in the next phases of study which may involve model understanding and validation after the primary data collection

A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms

Energy consumption in wireless IoT- a review

The Internet of Things (IoT) is a rising innovation, including a wide range of uses identified with modern control, savvy metering, home mechanization, horticulture, health, etc. For these applications to run independent the IoT gadgets are required to make do for a considerable length of time and years under severe vitality imperatives. When growing such applications, it is significant for the application to think about its own energy consumption. In this work, we propose and assess an energy consumption estimation approach for occasional detecting applications running on the IoT gadgets. Our methodology depends on three stages. In the main stage, we distinguish the unmistakable exercises, for example, rest, transmit, detect and process in a detecting cycle. In the subsequent stage, we measure the power consumption of these exercises before the IoT gadget has been conveyed in the arrange. The third stage happens at run-time once the IoT gadget has been sent, to convey the energy consumption of a detecting cycle. The energy consumption is determined by utilizing the exercises control and their spans acquired at run-time. The proposed methodology is basic and conventional on the grounds that it doesn’t include any intricate equipment for runtime control estimation. Besides, this methodology likewise consolidates the dynamic idea of detecting applications by run-time estimation of energy consumption

Some intriguing high-throughput DNA sequence variants prediction over protein functionality

This paper intends to review computational methods and high throughput automated tools for precisely prediction various functionalities of uncharacterized proteins based on their desired DNA sequence information alone. Then proposes a hybrid weighted network and Genetic Algorithm to improve prediction purpose. The main advantage of the method is the protein function and DNA sequence prediction can be computed precisely using best fitness parent in genetic algorithm. With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased exponentially and the pace is much slower in determining their biological attributes. The gap between DNA sequence variants and their functionalities has become increasingly large. However, detection of sequences based on protein data bank has become benchmark for many researchers. As amount of DNA sequence data continues to increase, the fundamental problem stay at the front of genome analysis. In the course of developing these methods, the following matters were often needed to consider: benchmark dataset construction, gene sequence prediction, operating algorithm, anticipated accuracy, gene recommender and functional integrations. In this review, we are to discuss each of them, with a different focus on operational algorithms and how to increase the accuracy of DNA sequence variants predictio

Quality of service provisioning in optical burst switching network

Future trend in communication system is to move to all-optical network. Optical Burst Switching (OBS) has been proposed as a new paradigm for switching and data transfer in all-optical network. One of the main challenges in deploying OBS network is to ensure QoS guarantee in minimising the contention and latency. The objective of this thesis is to develop OBS network that can ensure Quality-of- Service (QoS) by designing the ingress node that minimises delay and core node that minimises contention. In the ingress node, priority queueing (PQ) and burst assembly are deployed in differentiated service aware environment to decrease packet delay. Meanwhile, in the core node, integral of Fiber Delay Line, Wavelength Assignment and Wavelength Preemption (FDL-WA-WPremp) are proposed to minimise Burst Loss Probability (BLP) due to contention. In FDL, contending bursts are sent to travel over a longer fiber line and are, thus, delayed for a specific amount of time. In wavelength assignment, each traffic class has a pre-assigned wavelength for the transmission of bursts. Finally the wavelength preemption technique allows the higher priority traffic to preempt the lower priority traffic, when there is contention or, no available bandwidth for the transmission of the higher priority traffic. Three DiffServ class types that represent multimedia applications, including real time constant bit rate Expedited Forwarding (EF) traffic, real-time variable bit rate Assured Forwarding (AF) traffic and non-real time Best Effort (BE) traffic are investigated in the study. The proposed OBS network and traffic models have been developed using JAVA platform simulator and validated with mathematical analysis. The proposed OBS network performance parameters have been analysed based on BLP, packet end-to-end delay, bandwidth utilisation and throughput. The results show that the proposed OBS network with FDL_WA_WPremp, PQ and hybrid burst assembly; that is an event where burst is generated whenever the maximum assembling time is achieved or a minimum burst size is obtained, whichever occurs first, significantly improve the OBS network throughput by 10% compared to the technique without contention resolution. The proposed OBS network enhances the performance of EF, AF and BE traffic with BLP reduction of up to 70%, 42% and 34% respectively compared with OBS network with FDL only. In addition, the endto end delay performance of the EF and AF traffic deploying FDL_WA_WPremp with hybrid based assembly environment give 21.7% and 17.2% improvement compared to FDL_WA_WPremp with timer based. The overall findings prove that QoS provisioning can be guaranteed through FDL_WA_WPremp, PQ and hybrid burst assembly in OBS network. The proposed OBS network can therefore be deployed in the future all-optical network

Contention resolution in OBS using FDL-WA-WPREMP in hybrid based burst assembly

As optical burst switched networks offer connectionless transport, there exists the chance that burst may contend with one another at intermediate nodes. Contention will happen if multiple bursts from various input ports are meant for the same output port all at once. We introduce a new approach FDL-WA-WPremp to reduce the burst loss probability during contention. Through simulation and analytical modeling, it is shown that the policy introduced reduces burst loss substantially when compared to the standard policy of dropping the contending burst in the event of contention