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International Journal of Informatics and Communication Technology
Number of Followers: 4  Open Access journalISSN (Print) 2252-8776 - ISSN (Online) 2722-2616 Published by Institute of Advanced Engineering and Science  [11 journals]
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- A serious game about Covid-19: design and evaluation study
Authors: Farida Bouroumane, Mustapha Abarkan
Pages: 195 - 204
Abstract: As many countries experience the emergence of new waves of Covid-19, many governments around the world have reminded their citizens of the need for an engaging intervention that could improve compliance with Covid-19 safe behaviors using the media general public or social media. In the face of the serious threat of Covid-19, immunity issues are currently the subject of various research and studies. A promising approach is to use video game culture to educate and train citizens to healthily adopt eating habits to strengthen the immune system. The objective of this study is to develop a prototype of a serious game (SG) on how to strengthen the immune defenses in order to be able to fight a coronavirus infection and to constitute an anti-virus barrier. After defining the learning objectives by interviewing the stakeholders, we searched the scientific literature to establish the relevant theoretical bases. The learning contents have been validated by biology teachers. The learning mechanisms were then determined based on the learning objectives. The obtained experimental results show that 92% of the participants in the study have appreciated the quality of the scenario and the way in which the concept of interaction between the different game elements was presented.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp195-204
Issue No: Vol. 12, No. 3 (2023)
- Improved distribution and food safety for beef processing and management
using a blockchain-tracer support framework
Authors: Arnold Adimabua Ojugo, Patrick Ogholuwarami Ejeh, Odiakaose Chukwufunaya Christopher, Andrew Okonji Eboka, Frances Uchechukwu Emordi
Pages: 205 - 213
Abstract: Agriculture has since become a major source of livelihood for Nigerians. It also accounts for over 85% of the total food consumed within her borders. The sector has maintained improved productivity and profitability via a concerted effort to address critical issues such as an unorganized regulatory system, lack of food safety data, no standards in agricultural produce, non-adaptation to precision farming, and non-harmony via inventory trace supports. This study proposes blockchain-based trace-support in a continued effort to ensure food quality, consumer safety, and trading of food assets. It uses the radio frequency identification (RFID) sensor to register and track livestocks, farms/farmers, and abattoir processes as well as provisions a databank to trace livestock data. Results show the model adequately perform about 1,101 transactions per seconds with a response time of 0.21 s for queries and 0.28 s for https pages respectively for 2,500 users. Also, it yields a slightly longer time of 0.32 s for queries and 0.38 s for https pages respectively with an increased 5,000 users via the world-state as stored in the blockchain’s hyper-fabric ledger. Overall, the framework can directly query and retrieve data without it traversing the whole ledger. This, in turn, improves the efficiency and effectiveness of the traceability system.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp205-213
Issue No: Vol. 12, No. 3 (2023)
- Acceleration of convolutional neural network based diabetic retinopathy
diagnosis system on field programmable gate array
Authors: Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud
Pages: 214 - 224
Abstract: Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp214-224
Issue No: Vol. 12, No. 3 (2023)
- 2D router chip design, analysis, and simulation for effective
communication
Authors: Prateek Agarwal, Tanuj Kumar Garg, Adesh Kumar
Pages: 225 - 235
Abstract: The router is a network device that is used to connect subnetwork and packet-switched networking by directing the data packets to the intended IP addresses. It succeeds the traffic between different systems and allows several devices to share the internet connection. The router is applicable for the effective commutation in system on chip (SoC) modules for network on chip (NoC) communication. The research paper emphasizes the design of the two dimensional (2D) router hardware chip in the Xilinx integrated system environment (ISE) 14.7 software and further logic verification using the data packets transmitted from all input/output ports. The design evaluation is done based on the pre-synthesis device utilization summary relating to different field programmable gate array (FPGA) boards such as Spartan-3E (XC3S500E), Spartan-6 (XC6SLX45), Virtex-4 (XC4VFX12), Virtex-5 (XC5VSX50T), and Virtex-7 (XC7VX550T). The 64-bit data logic is verified on the different ports of the router configuration in the Xilinx and Modelsim waveform simulator. The Virtex-7 has proven the fast-switching speed and optimal hardware parameters in comparison to other FPGAs.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp225-235
Issue No: Vol. 12, No. 3 (2023)
- 180 nm NMOS voltage-controlled oscillator for phase-locked loop
applications
Authors: Ezzidin Hassan Aboadla, Ali Hassan
Pages: 236 - 241
Abstract: The voltage-controlled oscillator (VCO) is the primary device in the phase-locked loop (PLL) to produce the local oscillator frequency. The excessive phase noise of VCOs is the primary cause of PLL performance loss. This paper proposes the design and optimization of low phase noise and low power consumption for a 180 nm N-channel metal-oxide semiconductor NMOS VCO for PLL applications with P-channel metal-oxide semiconductor PMOS varactors and spiral inductors. At 2 V supply voltage, the optimized NMOS VCO has a power consumption of 21 mW, a phase noise of -130 dBc/Hz at 1 MHz offset and a total harmonic distortion (THD) of 3.9%. The proposed design is verified by PSpice simulations. A new criterion is proposed for optimizing NMOS LC oscillators.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp236-241
Issue No: Vol. 12, No. 3 (2023)
- A comprehensive survey of automatic dysarthric speech recognition
Authors: Shailaja Yadav, Dinkar Manik Yadav, Kamalakar Ravindra Desai
Pages: 242 - 250
Abstract: The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic dysarthric speech recognition (DSR) using machine learning (ML) and deep learning (DL) paradigms. It focuses on the methodology, database, evaluation metrics, and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR. The performance of the various machine and DL schemes is evaluated for the DSR on UASpeech dataset based on accuracy, precision, recall, and F1-score. It is observed that the DL based DSR schems outperforms the ML based DSR schemes.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp242-250
Issue No: Vol. 12, No. 3 (2023)
- CNN inference acceleration on limited resources FPGA platforms_epilepsy
detection case study
Authors: Afef Saidi, Slim Ben Othman, Meriam Dhouibi, Slim Ben Saoud
Pages: 251 - 260
Abstract: The use of a convolutional neural network (CNN) to analyze and classify electroencephalogram (EEG) signals has recently attracted the interest of researchers to identify epileptic seizures. This success has come with an enormous increase in the computational complexity and memory requirements of CNNs. For the sake of boosting the performance of CNN inference, several hardware accelerators have been proposed. The high performance and flexibility of the field programmable gate array (FPGA) make it an efficient accelerator for CNNs. Nevertheless, for resource-limited platforms, the deployment of CNN models poses significant challenges. For an ease of CNN implementation on such platforms, several tools and frameworks have been made available by the research community along with different optimization techniques. In this paper, we proposed an FPGA implementation for an automatic seizure detection approach using two CNN models, namely VGG-16 and ResNet-50. To reduce the model size and computation cost, we exploited two optimization approaches: pruning and quantization. Furthermore, we presented the results and discussed the advantages and limitations of two implementation alternatives for the inference acceleration of quantized CNNs on Zynq-7000: an advanced RISC machine (ARM) software implementation-based ARM, NN, software development kit (SDK) and a software/hardware implementation-based deep learning processor unit (DPU) accelerator and DNNDK toolkit.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp251-260
Issue No: Vol. 12, No. 3 (2023)
- Smart portable system for monitoring vibration based on the Raspberry Pi
microcomputer and the MEMS accelerometer
Authors: Hajar Baghdadi, Karim Rhofir, Mohamed Lamhamdi
Pages: 261 - 271
Abstract: In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp261-271
Issue No: Vol. 12, No. 3 (2023)
- Evaluating the impact of COVID-19 on the monetary crisis by machine
learning
Authors: Milad Mohseni
Pages: 272 - 283
Abstract: In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp272-283
Issue No: Vol. 12, No. 3 (2023)
- Realization of an intelligent evaluation system
Authors: Otman Maarouf, Rachid El Ayachi, Mohamed Biniz
Pages: 284 - 292
Abstract: A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for the ranking. However, existing computerized assessment systems do not provide all kinds of questions, namely open questions that require writing solutions. To overcome the challenges of the existing, the objective of this work is to achieve an intelligent evaluation system (IES) responding to the problems identified, and which adapts to the different types of questions, especially open-ended questions of which the answer requires sentence writing or programming.
PubDate: 2023-12-01
DOI: 10.11591/ijict.v12i3.pp284-292
Issue No: Vol. 12, No. 3 (2023)
