The use of the Amazon Web Services cloud enables new functionalities that are not possible with traditional solutions: low latency, local data processing and storage, and direct connectivity to other cloud services. Reimagining the way IoT connectivity services are presented by combining AWS cloud technology with mobile connectivity offers rapid prototyping to help connect devices natively over Wi-Fi. For this, the MQTT communication protocol is used to interact with the IoT device and exchange data, which allows controlling the basic functions of a sensor node. The installation is realized through a software development kit (SDK), which allows the creation of an application for Android devices. This solution gives the option to integrate together, improving the connectivity of the IoT system. The results enable board logging and network configuration, and can also be used to control the IoT device. The embedded firmware provides the required security functions.

Pérez, E., Araiza, J. C., Pozos , D., Bonilla, E., Hernández, J. C., & Cortes, J. A. (2021). Application for functionality and registration in the cloud of a microcontroller development board for iot in AWS. Applied Computer Science, 17(2), 14-27. https://doi.org/10.23743/acs-2021-10

Pérez, Elizabeth, Juan C. Araiza, Dreysy Pozos , Edmundo Bonilla, José C. Hernández, and Jesús A. Cortes. "Application for Functionality and Registration in the Cloud of a Microcontroller Development Board for Iot in Aws." Applied Computer Science 17, no. 2 (2021): 14-27.

E. Pérez, J. C. Araiza, D. Pozos , E. Bonilla, J. C. Hernández, and J. A. Cortes, "Application for functionality and registration in the cloud of a microcontroller development board for iot in AWS," Applied Computer Science, vol. 17, no. 2, pp. 14-27, 2021, doi: 10.23743/acs-2021-10.

Pérez E, Araiza JC, Pozos D, Bonilla E, Hernández JC, Cortes JA. Application for functionality and registration in the cloud of a microcontroller development board for iot in AWS. Applied Computer Science. 2021;17(2):14-27.

Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system .The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR).The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.

Al-Awad, N. A., Abboud, I. K., & Al-Rawi, M. F. (2021). Genetic Algorithm-PID controller for model order reduction pantographcatenary system. Applied Computer Science, 17(2), 28-39. https://doi.org/10.23743/acs-2021-11

Al-Awad, Nasir A., Izz K. Abboud, and Muaayed F. Al-Rawi. "Genetic Algorithm-Pid Controller for Model Order Reduction Pantographcatenary System." Applied Computer Science 17, no. 2 (2021): 28-39.

N. A. Al-Awad, I. K. Abboud, and M. F. Al-Rawi, "Genetic Algorithm-PID controller for model order reduction pantographcatenary system," Applied Computer Science, vol. 17, no. 2, pp. 28-39, 2021, doi: 10.23743/acs-2021-11.

Al-Awad NA, Abboud IK, Al-Rawi MF. Genetic Algorithm-PID controller for model order reduction pantographcatenary system. Applied Computer Science. 2021;17(2):28-39.

The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals.

Tellakula, K. K. P., Kumar, S., & Deb, S. (2021). A survey of ai imaging techniques for COVID-19 diagnosis and prognosis. Applied Computer Science, 17(2), 40-55. https://doi.org/10.23743/acs-2021-12

Tellakula, K K Praneeth, Saravana Kumar, and Sanjoy Deb. "A Survey of Ai Imaging Techniques for Covid-19 Diagnosis and Prognosis." Applied Computer Science 17, no. 2 (2021): 40-55.

K. K. P. Tellakula, S. Kumar, and S. Deb, "A survey of ai imaging techniques for COVID-19 diagnosis and prognosis," Applied Computer Science, vol. 17, no. 2, pp. 40-55, 2021, doi: 10.23743/acs-2021-12.

Tellakula KKP, Kumar S, Deb S. A survey of ai imaging techniques for COVID-19 diagnosis and prognosis. Applied Computer Science. 2021;17(2):40-55.

The rapid and uncontrollable cell division that spreads to surrounding tissues medically termed as malignant neoplasm, cancer is one of the most common diseases worldwide. The need for effective cancer treatment arises due to the increase in the number of cases and the anticipation of higher levels in the coming years. Oncolytic virotherapy is a promising technique that has shown encouraging results in several cases. Mathematical models of virotherapy have been widely developed, and one such model is the interaction between tumor cells and oncolytic virus. In this paper an artificially optimized Immune- Linear Quadratic Regulator (LQR) is introduced to improve the outcome of oncolytic virotherapy. The control strategy has been evaluated in silico on number of subjects. The crow search algorithm is used to tune immune and LQR parameters. The study is conducted on two subjects, S1 and S3, with LQR and Immune-LQR. The experimental results reveal a decrease in the number of tumor cells and remain in the treatment area from day ten onwards, this indicates the robustness of treatment strategies that can achieve tumor reduction regardless of the uncertainty in the biological parameters.

Hussein, M. A., Karam, E. H., & Habeeb, R. S. (2021). Cancer growth treatment using Immune Linear Quadratic Regulator based on crow search optimization algorithm. Applied Computer Science, 17(2), 56-69. https://doi.org/10.23743/acs-2021-13

Hussein, Mohammed A., Ekhlas H. Karam, and Rokaia S. Habeeb. "Cancer Growth Treatment Using Immune Linear Quadratic Regulator Based on Crow Search Optimization Algorithm." Applied Computer Science 17, no. 2 (2021): 56-69.

M. A. Hussein, E. H. Karam, and R. S. Habeeb, "Cancer growth treatment using Immune Linear Quadratic Regulator based on crow search optimization algorithm," Applied Computer Science, vol. 17, no. 2, pp. 56-69, 2021, doi: 10.23743/acs-2021-13.

Hussein MA, Karam EH, Habeeb RS. Cancer growth treatment using Immune Linear Quadratic Regulator based on crow search optimization algorithm. Applied Computer Science. 2021;17(2):56-69.

The issue of planning assembly operations remains crucial decision-making area for many of manufacturing companies. It becomes particularly significant in case of small and medium enterprises that perform unit or small-scale production, where the option of applying specialized software is often very limited – both due to high purchase price, but also due to its applicability to single unit manufacturing, that is executed based on individual customer orders. The present article describes the possibility of applying the MS Excel spreadsheet in the planning of machine assembly processes. It emphasises, in particular, the method for using the spreadsheet in subsequent stages of the process, and the identification of possible causes that have impact on problems with the planning process. We performed our analysis on the basis of actual data from one of the machine industry enterprises that manufactures in central Poland.

Brzozowska, J., & Gola, A. (2021). Computer aided assembly planning using Ms Excel software – a case study. Applied Computer Science, 17(2), 70-89. https://doi.org/10.23743/acs-2021-14

Brzozowska, Jolanta, and Arkadiusz Gola. "Computer Aided Assembly Planning Using Ms Excel Software – a Case Study." Applied Computer Science 17, no. 2 (2021): 70-89.

J. Brzozowska and A. Gola, "Computer aided assembly planning using Ms Excel software – a case study," Applied Computer Science, vol. 17, no. 2, pp. 70-89, 2021, doi: 10.23743/acs-2021-14.

Brzozowska J, Gola A. Computer aided assembly planning using Ms Excel software – a case study. Applied Computer Science. 2021;17(2):70-89.

This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.

Sridharan, M., Rani Arulanandam, D. C., Chinnasamy, R. K., Thimmanna, S., & Dhandapani, S. (2021). Recognition of font and tamil letter in images using deep learning. Applied Computer Science, 17(2), 90-99. https://doi.org/10.23743/acs-2021-15

Sridharan, Manikandan, Delphin Carolina Rani Arulanandam, Rajeswari K Chinnasamy, Suma Thimmanna, and Sivabalaselvamani Dhandapani. "Recognition of Font and Tamil Letter in Images Using Deep Learning." Applied Computer Science 17, no. 2 (2021): 90-99.

M. Sridharan, D. C. Rani Arulanandam, R. K. Chinnasamy, S. Thimmanna, and S. Dhandapani, "Recognition of font and tamil letter in images using deep learning," Applied Computer Science, vol. 17, no. 2, pp. 90-99, 2021, doi: 10.23743/acs-2021-15.

Sridharan M, Rani Arulanandam DC, Chinnasamy RK, Thimmanna S, Dhandapani S. Recognition of font and tamil letter in images using deep learning. Applied Computer Science. 2021;17(2):90-99.

Lending systems in real world are not much secure and reliable as the borrower and third parties involved in this aspect may create various deceitful situations. Blockchain is a secure system where the utilization of smart contract can avoid deceptive phenomena involved in lending but the decline in exchange rate of cryptocurrency can create the opportunity to pay back less than the borrowed amount in terms of fiat money. In this paper, a blockchain and smart contract-based lending framework is designed which requires the borrower to provide Ethereum Request for Comments (ERC)-20 standard tokens as collateral to mitigate the associated risks. The smart contract feature is utilized to automate the system without any third-party management. Besides, transaction stored in the blocks creates transparency among the users of the system. To tackle the aforementioned issues, ERC-20 token value is increased periodically and the instability of the exchange rate is surveilled by the system. By the end of this paper, some test cases and charts relevant to the data set are evaluated to assess the effectiveness of the system.

Reno, S., Chowdhury, S. S. R. A., & Sadi, I. (2021). Mitigating loan associated financial risk using blockchain based lending system. Applied Computer Science, 17(2), 100-126. https://doi.org/10.23743/acs-2021-16

Reno, Saha, Sheikh Surfuddin Reza Ali Chowdhury, and Iqramuzzaman Sadi. "Mitigating Loan Associated Financial Risk Using Blockchain Based Lending System." Applied Computer Science 17, no. 2 (2021): 100-126.

S. Reno, S. S. R. A. Chowdhury, and I. Sadi, "Mitigating loan associated financial risk using blockchain based lending system," Applied Computer Science, vol. 17, no. 2, pp. 100-126, 2021, doi: 10.23743/acs-2021-16.

Reno S, Chowdhury SSRA, Sadi I. Mitigating loan associated financial risk using blockchain based lending system. Applied Computer Science. 2021;17(2):100-126.