iosr-JCE   Volume-24 ~ Issue-4 ~ Jul. - Aug. 2022

Abstract: The present work includes the case study of threshold and comfort level voltages for the electrodes of cochlear implant for one Blue Hearing System(BHS) user. The patient after surgical implantation has been observed at regular intervals at the time of mapping. The present study shows that the comfort level voltages are always set at higher level in comparison to the threshold level voltages. Later in this study it has been observed that the variation between voltage levels of comfort level or threshold level for all electrodes during two consecutive mapping reduces significantly and becomes almost fixed after few regular mappings

Keywords: Blue Hearing System, Cochlear Implant, Mapping, Threshold Level, Comfort Level

[1]. Saxena P.,Mehta A, Refinement of Input Speech by Suppressing the Unwanted Amplitudes for Blue Hearing System, Proceedings of International conference, IEEE, 2016.
[2]. Zeng Fan-Gang, Rebscher Stephen, Harrison William V., Sun Xiaoan, Feng Haihong, Cochlear Implants :System Design, Integration and Evaluation, NIH Public Access Author Manuscript IEEE Rev Biomed Eng. 2008 January 1; 1: 115–142. doi:10.1109/RBME.2008.2008250.
[3]. Talha J. Ahmad, Hussnain Ali, Muhammad Asim Ajaz, Shoab A. Khan, Efficient Algorithm development of CIS Speech Processing Strategy for Cochlear Implants, 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.
[4]. Jennifer Mertes, AuD, Jill Chinnici, Cochlear Implants - Considerations in Programming for the Pediatric Population, February 13, 2006, https://www.audiologyonline.com/articles/ cochlear-implants-considerations-in-programming-1011/
[5]. V. R. Pradeep, A comparative Study on the Differences between NRT and Behavioral Mapping in Cochlear Implant- A Single Case Study, Global Journal of Otolaryngology, Vol.9 Issue No.4 , pp.01-04,2017.

Abstract: In a stack of prominent fields, there are irrational interest to treat notes to the PC storage circle of information that will be obtained in printed or composed with chronic hand or photographs to then reuse these real factors by PC. One important approach to storing information to a PC device from the print record can be the first to test the report after it makes it as an image archive. In any case to reuse this estimate, it will definitely test to take Gandand on or request text-based substances or various real factors from the image-chronic image. In this way the system is appropriately recovering and storing real factors, especially text, from photo notes needed. Individual.....

Keywords: OCR, Perspective Distortion, Image Binarization, Image Thresholding, Image Segmentation, MATLAB.

[1]. Sandeep Tiwari, Shivangi Mishra, Priyank Bhatia, Praveen Km. Yadav, Optical man or lady acknowledgment the use of MATLAB, global magazine of prevalent examination in Electronics and verbal trade Engineering (IJARECE) volume 2, issue 5 may likewise (2013)
[2]. H.F. Schantz, The historical backdrop of OCR fame age clients affiliation, Manchester center, VT, (2000)
[3]. Fawzi1 Mohamed Mohsen. A. Rashwan 1, Hany Ahmed 1, Shaimaa Samir 1, SherifM. Abdou2, Hassanin M. Computer based intelligence Barhamtoshy3, and Kamal M. Jambi3 technique in photo for camera-principally based OCR age, (2015) thirteenth worldwide gathering on report assessment and notoriety (ICDAR)
[4]. Santosh Kumar and B. Rama2 Hengel, Algorithms and assessment strategy innovation approched Comprative individual prevalence examines with OCR assessment IEEE overall show on energy Electronics. canny control and force structures (ICPEICES 2016)
[5]. N. Venkata Rao, Dr. A.S.C.S.Sastry, A.S.N.Chakravarthy, P kalyanchakravarthi, optical individual standing calculation methods, magazine hypothetical and did records age January 20 (2016). Vol.83. 2, ISSN: 1992-8645, e-ISSN: 1817-3195

Abstract: Digital literacy is indeed among the fastest common educational tools, attracting the interest of majority of mobile phone users; mobile platform allows student to develop anytime anywhere through the use of Internet access. Moreover, there is much inadequate study done to look into the aspects that influence students' Behavioral and attitude using active technology in NAUB. The purpose of this research is to look at the effects of mobile learning on students' academic achievement at NAUB University. A data analysis approach was employed to accomplish such, as well as the findings were evaluated using SPSS.Data were taken from 200 respondents at NAUB in order to evaluate the study model utilizing correlated and coefficients analyses. On several characteristics, majority students get to utilize smartphone learning: performance expectancy, effort expectancy, social impact, and technology competency.

Keywords: Mobile learning, E-learning, UTAUT

[1]. Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., & Shuib, L. (2021). For Sustainable Application of Mobile Learning : An Extended UTAUT Model to Examine the Effect of Technical Factors on the Usage of Mobile Devices as a Learning Tool.
[2]. Aliaño, Á. M., María, A., Hueros, D., Aguaded, I., & Guzmán, M. D. (2019). Mobile Learning in University Contexts Based on the Unified Theory of Acceptance and Use of Technology ( UTAUT ). 8(1), 7–17. https://doi.org/10.7821/naer.2019.1.317
[3]. Dogra, N. (2019). Consumer adoption of smartphone fi tness apps : an extended UTAUT2 perspective. December. https://doi.org/10.1108/JIBR-05-2018-0158
[4]. Farzin, S., & Dahlan, H. M. (2018). AN EMPIRICAL STUDY OF THE BEHAVIORAL FACTORS AFFECTING STUDENTS ' PERCEPTION ON ADOPTING ELECTRONIC ASSESSMENT SYSTEM. 96(1).
[5]. Liliana, L., Mada, U. G., Technology, I., Engineering, I., Santosa, P. I., Mada, U. G., Kusumawardani, S. S., & Mada, U. G. (2022). World Journal on Educational Technology : Current Issues Completion factor in massive open online course in developing countries : A literature review in 2015-2021. 14(2), 456–472.

Abstract: Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, communication locally and routed over the internet backbone. Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network. Also known as Edge Computing or fogging, fog computing facilitates the operation of compute, storage and networking services between end devices and cloud computing data centers. Fog computing (FC) is an emerging distributed computing platform aimed at bringingComputation close to its data sources, which can reduce....

Keywords: Fog computing, Cloud Computing, mobile computing Internet of Things, optimization

[1]. YANG LIU, JONATHAN E. FIELDSEND, (Member, IEEE), AND GEYONG MIN, (Member, IEEE)Department of Computer Science, University of Exeter, Exeter EX4 4QF, U.K.Corresponding author: Geyong Min (g.min@exeter.ac.uk)
[2]. R. Khan, S. U. Khan, R. Zaheer, and S. Khan, ""Future Internet:The Internet of Things architecture, possible applications and key chal-lenges,‟‟ inProc. FIT, Dec. 2012, pp. 257–260.
[3]. Uttam A. Deshmukh, Dr. Sunanda A. More,"Fog Computing: New Approach in the World of Cloud Computing"." ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering
[4]. W. Lumpkins, ""The Internet of Things meets cloud computing [stan-dards corner],‟‟IEEE Consum. Electron. Mag., vol. 2, no. 2, pp. 47–51,Apr. 2013.
[5]. H.-L. Truong and S. Dustdar, ""Principles for engineering IoTcloud systems,‟‟IEEE Cloud Comput., vol. 2, no. 2, pp. 68–76,Mar./Apr. 2015..

Abstract: Recently, face recognition systems (FRSs) have received significant attention from researchers due to their valuable benefits, such as monitoring and tracking banned individuals, observing borders, and controlling authenticated access to sensitive places. However, when using traditional techniques, FRS suffers from low accuracy. This struggle is because advanced attacks, such as morphing attacks and changes in luminance of the environment, are not considered. This work aims to build an FRS that can detect and recognize faces under an unconstrained environment using a CNN. The experimental results showed that the proposed FRS achieved high performance with an accuracy value of 99.7%. Thus, the proposed system was applied to morphed images to test its robustness against advanced attacks such as morphing. The face recognition results under morphing attacks showed a high accuracy of 95%..

Keywords: Face Recognition; Face Detection; Morphing Attack; CNN; Deep Learning; AI, Security

[1]. X. Sun, P. Wu, and S. C. Hoi, "Face detection using deep learning: Animproved faster rcnn approach," Neurocomputing, vol. 299, pp. 42–50, 2018.
[2]. G. Guo and N. Zhang, "A survey on deep learning based face recognition,"Computer vision and image understanding, vol. 189, p. 102805, 2019.
[3]. N. Dey, J. Chaki, L. Moraru, S. Fong, and X.-S. Yang, "Firefly algorithm and its variants in digital image processing: A comprehensive review,"Applications of firefly algorithm and its variants, pp. 1–28, 2020.
[4]. L. Rice, E. Wong, and Z. Kolter, "Overfitting in adversarially robust deep learning," in International Conference on Machine Learning. PMLR, 2020, pp. 8093–8104.
[5]. A. Rot and B. Olszewski, "Advanced persistent threats attacks in cyberspace. threats, vulnerabilities, methods of protection." in FedCSIS (Position Papers), 2017, pp. 113–117.

Abstract: Background: The application of a machine learning approaches in medical diagnostics is becoming increasingly popular. The paper discusses machine learning as a task for classification of complex objects. Deep neural networks are commonly used in tasks for classification of complex objects. Medical diagnostics can be considered as a classification of complex systems. The blood serum and the data obtained on the basis of its microcalorimetric analysis are difficult objects. These data make it possible to diagnose onco-disease to a certain extent.......

Keywords: Machine Learning; Deep Neural Networks; Oncodiagnosis.

[1]. Tom Mitchell, Machine Learning, McGraw Hill, 1997
[2]. N.Gabriel, N.F.Johnson Using Neural Architectures to Model Complex Dynamical Systems. Advances in Artificial Intelligence and Machine Learning. 2022;2(2):24.
[3]. Sergio Sanchez-Martinez , Oscar Camara, Gemma Piella, Maja Cikes, Miguel Ángel González-Ballester, Marius Miron, Alfredo Vellido, Emilia Gómez, Alan G. Fraser, Bart Bijnens. Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.. Frontiers in Cardiovascular Medicine. 04 January 2022. https://doi.org/10.3389/fcvm.2021.765693
[4]. Yu X, Efe MO, Kaynak O. A General Backpropagation Algorithm for Feedforward Neural Networks Learning. IEEE Transactions on Neural Networks. 2002;13:251–254.
[5]. McFall KS, Mahan JR. Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions. IEEE Transactions on Neural Networks. 2009;20:1221–1233

Abstract: The classical Hiemenz solution describes incompressible two-dimensional stagnation point flow at a solid wall. Blyth & Hall consider an unsteady version of this problem, examining particularly the response close to the wall when the solution at infinity is modulated in time by a periodic factor of specified amplitude and frequency. they find that there exists a threshold frequency above which the flow is regular and periodic, Our current study deals with the stagnation point flow in the presence of an oscillating motion at infinity, affected by the change in the oscillation frequency and the parameter of unsteadiness, in addition to an oscillatory function describing the suction and blowing at the wall, which changes by the effect of amplitude and frequency of oscillation. The set of influencing parameters was called the flow parameters set (FPS), as the study found a threshold separating the parameters that make the flow reversible from the parameters that make the flow non-reversible.

Key words. Stagnation Flow, Unsteadiness, Oscillation, Blowing.

[1]. Rott, N. (1956) Unsteady Viscous Flow in the Vicinity of a Stagnation Point, Quarterly of Applied Mathematics, Vol. 13, pp. 444- 451.
[2]. Yang K. T. (1958) Unsteady Laminar Boundary Layers in an Incompressible Stagnation Flow, JOTJHNAL OF APPLIED MECHANICS,Vol. 25, THANS. ASME, Vol. 80, Series E, pp. 1-7.
[3]. Williams J. C (1968) Nonsteady Stagnation-Point Flow, AIAA Journal, Vol. 6, pp. 2417-2419.
[4]. Riley N. and Vasantha R. (1989) An unsteady stagnation-point flow. Quarterly Journal of Mechanics and Applied Mathematics, 42:511–521.
[5]. Grosch C. E. and Salwen H. (1982) Oscillating stagnation point flow. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 384:175–190

Abstract: With the advent of technology revolution, modern industries are adopting new methods for detecting defects in the steel plates that have affected the inspection quality for a long time. The new methods include use of AI Machine learning, image processing, deep learning, neural network and many other processes. After the steel is freshly cut there could be several defects like stains and scratches on the surface of the steel that could make it ineffective for usage purposes. The manual methods are very time taking and labour intensive and prone to unattended defects hence the attempts to find alternative solutions that are more effective and beneficial for the overall steel industry.......

[1]. Luo, Q.; Fang, X.; Liu, L.; Yang, C.; Sun, Y. Automated Visual Defect Detection for Flat Steel Surface: A Survey. IEEE Trans.
[2]. Instrum. Meas. 2020, 69, 626–644. [CrossRef]
[3]. Thomas, G.B.; Jenkins, M.S.; Mahapatra, R.B. Investigation of strand surface defects using mould instrumentation and modelling.
[4]. Ironmak. Steelmak. 2004, 31, 485–494. [CrossRef]
[5]. Shi, T.; Kong, J.; Wang, X.; Liu, Z.; Zheng, G. Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy. J. Cent. South Univ. 2016, 23, 2867–2875. [CrossRef]

Abstract: Background: Environmental monitoring remains one of the important problems of the modern world, in the management of which information systems play an important role. Raw data received from various sensor devices need to be processed with appropriate methods and techniques. The variety of sensor devices used for monitoring generates a variety of (non-uniform in format) data streams. This, in turn, complicates the analysis process. Materials and Methods: The method of analytical heuristics is used to achieve a uniform presentation of data. This method allows all data types to be represented in the same format. Many data analysis techniques can be used to process homogeneous data. including a description of the state of the environment in the form of concepts.

Key Word: Ecomonitoring; Data Uniform; Concept; Data Analyses

[1]. El-Gayar, Omar and Fritz, Brian D. (2006) "Environmental Management Information Systems (EMIS) for Sustainable Development: A Conceptual Overview," Communications of the Association for Information Systems: Vol. 17 , Article 34. DOI: 10.17705/1CAIS.01734 Available at: https://aisel.aisnet.org/cais/vol17/iss1/34
[2]. Lucivero, F. Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives. Sci Eng Ethics 26, 1009–1030 (2020). https://doi.org/10.1007/s11948-019-00171-7
[3]. Guidelines for drinking-water quality: Fourth edition incorporating the first and second addenda, Geneva, 2022. https://www.who.int/publications/i/item/9789240045064
[4]. Chavchanidze V. V. Analiticheskiye evristiki iskussnvennogo intellekta pri formirovanii ponyatiy, opoznanii obrazov i klassifikatsii obyektov. VINITI. № 2080-70 Dep. (Analytical Heuristics of the Artificial Intellect in the Formation of Notions, Identification of Images and Classification of Objects, VINITI, #2080-70 Dep.
[5]. Chavchanidze V. V. Analiticheskoye Resheniye Zadachi Formirovaniya Ponyatyi i Raspoznavaniya Obrazov (Analytical Resolution of the Problem of Identification of Notions and Recognition of Images). Proceedings of the Academy of Sciences of the Georgian SSR, vol.63, No.1, 1971.

Abstract: An amazing digital multimedia revolution marked the end of the 20th century. In this revolution, images are extremely important to communicate in the present era of multimedia. When a person transfers images over an unsecured communication network, maintaining image confidentially is a difficult task.[1]In this paper, the Rubik's cube algorithm's principle is applied to the image pixel value of Color,greyscale and text picture images. The XNOR operation is then performed on the scrambled image using two secret keys. Secret keys are generated using a random number and can also be manually selected. Decrypting images requires both keys and the number of iterations performed by the algorithm. Finally, the experimental results and security analysis show how important keys are in encryption or decryption. Whenever it comes to attacks, images are highly secure........

Keywords: Rubik's Cube, key Generation, Scrambled image, Encryption, Decryption

[1]. Adrian-Viorel Diaconu. and Khaled Loukhaoukha, "An Improved Secure Image Encryption Algorithm Based on Rubik's Cube Principle and Digital Chaotic Cipher", Mathematical Problems in Engineering Volume 2013, Article ID 848392,10 pages.
[2]. K. Loukhaoukha, J.-Y. Chouinard, and A. Berdai, "A secure image encryption algorithm based on Rubik's cube principle," Journal of Electrical and Computer Engineering, vol. 2012, Article ID 173931, 13 pages, 2012.
[3]. M.Sirisha, SVVS Lakshmi. "Pixel Transformation based on Rubik's Cube Principle", International Journal of Application or Innovation in Engineering &Management (IJAIEM) ,Vol.3, no. 5, pp. 273-277, 2014.
[4]. Shamsa Kanwal, Saba Inam, "Analytic Study of a Novel Color Image Encryption Method Based on the Chaos System and Color Codes", Complexity, vol. 2021, Article ID 5499538, 19 pages, 2021.
[5]. S. Gandhi. R. Gor, "Digital Image Encrtyption using Rubik's Cube Algorithm", IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 18, Issue 4 Ser. I (Jul. – Aug. 2022), PP 16-25. J. Cent. South Univ. 2016, 23, 2867–2875. [CrossRef]

Abstract: Artificial Intelligence is defined as the "study of intelligent agent: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals." AI in software testing tools make the software development life cycle easier by the use of reasoning, problem solving and in some cases machine learning. AI can reduce the amount of tedious task in testing. Applying AI will reduce the involvement of tester in most mundane tasks. AI also reduces failure rate and shortens software development process. Background: In 1980's the major model used in software development was waterfall model and testing was completely manual. .......

Keywords: Testim; Applitools; Test-Craft; Funtionize; Mabl

[1]. AI in Software Testing available at https://www.testingxperts.com/blog/AI-in-Software-Testing#How%20has%20AI%20evolved%20in%20software%20testing
[2]. Four Popular AI tools for Software Testing available at https://www.emtec.digital/think-hub/blogs/four-popular-ai-tools-for-software-testing/
[3]. 5 Popular AI-powered tools for test automation available at https://www.nextgenerationautomation.com/post/ai-powered-tools
[4]. what are the Advantages of Artificial Intelligence in Software Testing? Available at https://marutitech.com/artificial-intelligence-in-testing/
[5]. 8 benefits of AI in software testing available at https://www.analyticsinsight.net/8-benefits-of-ai-in-software-testing