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Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
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Gesture Language Translator using Morse Code

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Volume 8 Issue 3 September - November 2020

Research Paper

Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction

Chandu Vaidya * , Sajan Rangari , Tanmay Walke *, Tanmay Phadnis **, Yash Deshmukh *, Yash Kunte ****

*-****** Department of Computer Science Engineering, Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India.

Vaidya, C., Rangari, S., Walke, T., Phadnis, T., Deshmukh, Y., and Kunte, Y. (2020). Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction. i-manager's Journal on Computer Science, 8(3), 1-11. https://doi.org/10.26634/jcom.8.3.18048

Abstract

While ageing has a major impact on human health and the economy, little is known about its molecular basis: control and mechanism. More than 300 genes have been linked to human ageing to date (almost all of them function as protein-coding genes). Individual genes or small subsets of these genes linked to ageing have been extensively studied, but overall study of these genes has been restricted. To fill this gap, we looked at different applications of modern artificial intelligence (AI) algorithms in the field of ageing science. Biological Age (BA), also known as the hypothetical underlying age of an organism, has been calculated using a number of machine learning algorithms. Different circulating and non-circulating biomarkers can be used to calculate BA. Based on our study, we expect modern machine learning (ML) models to contribute to the reputation and prominence of healthcare and pharmaceutical longevity biotechnology, as well as the convergence of numerous fields of research. In this paper, we used a variety of machine learning algorithms on the skin fibroblasts cells dataset because it is ideal for age prediction studies for several reasons. These skin fibroblasts contain age-related disruption, contain age-dependent phenotypic, epigenomic, and transcriptomic changes, and it is easy to collect using non-invasive techniques. By using various machine learning models like elastic net, random forest regressor, support vector regressor and artificial neural network for calculating the accuracy of these models on the dataset, we obtain an accuracy of 72 percent with elastic net.

References

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[2]. Ashapkin, V. V., Kutueva, L. I., Kurchashova, S. Y., & Kireev, I. I. (2019). Are there common mechanisms between the Hutchinson–Gilford Progeria Syndrome and natural aging? Frontiers in Genetics, 10, 455. https://doi. org/10.3389/fgene.2019.00455

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[4]. Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., Videira, P. A., & Valadão, G. (2019). Artificial intelligence (AI) in rare diseases: Is the future brighter? Genes, 10(12), 978-1001. https://doi.org/10.3390/genes10120978

[5]. Brehme, M., Voisine, C., Rolland, T., Wachi, S., Soper, J. H., Zhu, Y., ..., & Morimoto, R. I. (2014). A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Reports, 9(3), 1135-1150. https://doi.org/10.1016/j.celrep.2014.09.042

[6]. Campbell, J. M., Mahbub, S., Habibalahi, A., Paton, S., Gronthos, S., & Goldys, E. (2020). Ageing human bone marrow mesenchymal stem cells have depleted NAD (P) H and distinct multispectral autofluorescence. GeroScience, 5(3), 1-10. https://doi.org/10.1007/s11357-020-00250-9

[7]. Childs, B. G., Durik, M., Baker, D. J., & Van Deursen, J. M. (2015). Cellular senescence in aging and age-related disease: From mechanisms to therapy. Nature Medicine, 21(12), 1424-1435. https://doi.org/10.1038/nm.4000

[8]. de Lucia, C., Murphy, T., Steves, C. J., Dobson, R. J., Proitsi, P., & Thuret, S. (2020). Lifestyle mediates the role of nutrient-sensing pathways in cognitive aging: Cellular and epidemiological evidence. Communications Biology, 3(1), 1-17. https://doi.org/10.1038/s42003-020-0844-1

[9]. Fabris, F., De Magalhães, J. P., & Freitas, A. A. (2017). A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188. https://doi.org/ 10.1007/s10522-017-9683-y

[10]. Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., ..., & Navlakha, S. (2018). Predicting age from the transcriptome of human dermal fibroblasts. Genome Biology, 19(1), 1-8. https://doi.org/10.1186/s1305 9-018-1599-6

[11]. Garitaonandia, I., Amir, H., Boscolo, F. S., Wambua, G. K., Schultheisz, H. L., Sabatini, K., ..., & Laurent, L. C. (2015). Increased risk of genetic and epigenetic instability in human embryonic stem cells associated with specific culture conditions. PloS One, 10(2). https://doi.org/10.1371/ journal.pone.011830

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[13]. Holder, L. B., Haque, M. M., & Skinner, M. K. (2017). Machine learning for epigenetics and future medical applications. Epigenetics, 12(7), 505-514. https://doi.org/ 10.1080/15592294.2017.1329068

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[15]. Jónsson, B. A., Bjornsdottir, G., Thorgeirsson, T. E., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D. F., ..., & Ulfarsson, M. O. (2019). Brain age prediction using deep learning uncovers associated sequence variants. Nature Communications, 10(1), 1-10. https://doi.org/10.1038/s4 1467-019-13163-9

[16]. Naue, J., Hoefsloot, H. C., Mook, O. R., Rijlaarsdam- Hoekstra, L., van der Zwalm, M. C., Henneman, P., ..., & Verschure, P. J. (2017). Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression. Forensic Science International: Genetics, 31, 19-28. https://doi.org/10.1016 /j.fsigen.2017.07.015

[17]. Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., ..., & Fedichev, P. O. (2018). Extracting biological age from biomedical data via deep learning: Too much of a good thing? Scientific Reports, 8(1), 1-11. https://doi.org/10.1038/s41598-018-23534-9

[18]. Rahman, S. A. (2019). Quantifying human biological age: A machine learning approach. [Doctoral dissertation]. West Virginia University, Morgantown, WV.

[19]. Rebelo-Marques, A., De Sousa Lages, A., Andrade, R., Ribeiro, C. F., Mota-Pinto, A., Carrilho, F., & Espregueira- Mendes, J. (2018). Aging hallmarks: The benefits of physical exercise. Frontiers in Endocrinology, 9, 258. https:// doi.org/10.3389/fendo.2018.00258

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[21]. Schulz, R., Wahl, H. W., Matthews, J. T., De Vito Dabbs, A., Beach, S. R., & Czaja, S. J. (2015). Advancing the aging and technology agenda in gerontology. The Gerontologist, 55(5), 724-734. https://doi.org/10.1093/geront/gnu071

[22]. Tang, W., Chen, J., & Hong, H. (2021). Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods. Chemosphere, 273, 128567. https://doi.org/10. 1016/j.chemosphere.2020.128567

[23]. Tchkonia, T., & Kirkland, J. L. (2018). Aging, cell senescence, and chronic disease: Emerging therapeutic strategies. Journal of American Medical Association, 320(13), 1319-1320. https://doi.org/10.1001/jama.2018.12440

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Research Paper

Verification for Online Signature Biometrics Using Deep Learning

Samatha Juluri* , Voladri Prashanth Reddy , Dhanunjay Sagar *, Gattu Sai Koundinya ****

*-**** Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, Telangana, India.

Juluri, S., Reddy, V. P., Sagar, D., and Koundinya, G. S. (2020). Verification for Online Signature Biometrics Using Deep Learning. i-manager's Journal on Computer Science, 8(3), 12-18. https://doi.org/10.26634/jcom.8.3.18074

Abstract

Nowadays, human identification is required for daily routine activities such as entering secured locations in addition to many other applications. To achieve biometric verification, higher security levels with easier user interaction is needed. Biometric verification help identifying people based on their extracted physical or behavioural features. These features should have certain properties such as uniqueness, permanence, acceptability, collectability, and affordable cost to employ any biometric system. Handwritten signatures are treated as the most natural method of verifying a person's identity as compared to other biometric and cryptographic forms of verification. The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures. This paper presents a method for verifying handwritten signatures by using a Neural Network architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. The normal Handwritten Signature Verification (HSV) uses image processing technique to verify the signatures. This takes much processing and time to differentiate the genuine and forged signatures. Keeping this in mind, we refurbished the existing system and made necessary improvements to modernize the processes. Such as through Deep Learning techniques, we can deal with signature verification more accurately as it has many algorithms to check the accuracy of the results.

References

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[9]. Rizvi, M. S. Z. (2020, February 18). Learn image classification on 3 datasets using Convolutional Neural Networks (CNN). Analytics Vidhya. Retrieved from https:// www.analyticsvidhya.com/blog/2020/02/learn-imageclassification- cnn-convolutional-neural-networks-3- datasets/

[10]. Sanda, S., & Amirisetti, S. (2017). Online handwritten signature verification system: Using Gaussian mixture model and longest common sub-sequences (Postgraduate Thesis). Blekinge Institute of Technology, Sweden.

[11]. Sayeed, S., Samraj, A., Besar, R., & Hossen, J. (2010). Online hand signature verification: A review. Journal of Applied Sciences, 10(15), 1632-1643. https://doi.org/10. 3923/jas.2010.1632.1643

[12]. Sundararajan, K., & Woodard, D. L. (2018). Deep learning for biometrics: A survey. ACM Computing Surveys (CSUR), 51(3), 1-34. https://doi.org/10.1145/3190618

[13]. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Ortega- Garcia, J. (2018). Exploring recurrent neural networks for online handwritten signature biometrics. IEEE Access, 6, 5128- 5138. https://doi.org/10.1109/ACCESS.2018.2793966

[14]. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Ortega- Garcia, J. (2021). DeepSign: Deep on-line signature verification. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(2), 229-239. https://doi.org/10.1109/ TBIOM.2021.3054533

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Research Paper

Design and Implementation of Monolithic Architecture based Hybrid Application for End-to-End Automated Management of Various College Events

Raghul Karthik N.* , Rathina Karthi T. , Kumaresan G. *, Vishal Aakash A. ****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamilnadu, India.

Karthik, N. R., Karthi, T. R., Kumaresan, G., and Aakash, A. V. (2020). Design and Implementation of Monolithic Architecture based Hybrid Application for End-to-End Automated Management of Various College Events. i-manager's Journal on Computer Science, 8(3), 19-24. https://doi.org/10.26634/jcom.8.3.18119

Abstract

In recent times, various academic or non-academic events are conducted virtually by most of the colleges/institutions/ organizations/schools, to disseminate new concepts and ideas where the participating individuals can gain knowledge in respective disciplines. A software which does the functions done by an event manager is said to be an online event management system. The idea behind this proposed work is to make the participants, student coordinator, event organizing staff/faculty coordinators and head of the department to access the event, effortlessly. The aim of the project is to automate all event activities right from event registration to certificate distribution. Manual exertion will be automated and no workload will exist among the organizers and participants, so that the college management could plan the events online and student volunteers could be assigned tasks transparently. Students will be able to register for multiple events from various colleges and book themselves for the event through this application. Furthermore, students will be able to track their past events and to attend quiz competitions from their app itself. The authenticity of students for their registration and payment status can be verified through QR Scanner and also the students can attend the event using the QR Code. Real time communication between the students and the event organizers can be achieved through this proposed work.

References

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Review Paper

Evolution of OS Architectures: A Survey on Parallel Computing based OS Kernels

Rohma Najam * , Ebrahim Deen **

*-** Bahria University, Karachi, Pakistan.

Najam, R., and Deen, E. (2020). Evolution of OS Architectures: A Survey on Parallel Computing based OS Kernels. i-manager's Journal on Computer Science, 8(3), 25-43. https://doi.org/10.26634/jcom.8.3.18252

Abstract

Parallel and multiprocessor computing has always been considered as the pioneer machines for speed, performance and optimization. Of course, to run such hardcore machines, sophisticated software, known as Operating Systems (OS) are required. In this paper some well known operating system which are specifically developed and used for parallel and multiprocessor computing have been reviewed. Specific functionalities of the OS kernels such as interprocess communication, architectures and memory management have been discussed in this survey paper.

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A Survey of Security Threats in Distributed Operating System

Sumra Khan * , Syed Muhammad Waqas , Arbaz Ahmed*

*-*** Department of Computer Science, Bahria University, Karachi, Pakistan.

Khan, S., Waqas, S. M., and Ahmed, A. (2020). A Survey of Security Threats in Distributed Operating System. i-manager's Journal on Computer Science, 8(3), 44-60. https://doi.org/10.26634/jcom.8.3.18262

Abstract

Recently, security in software is becoming complex, especially in distributed computing. A secured and trusted environment is critically important for distributed computing. Different services and resources in the distributed system have to be secure at different levels; therefore, in this paper, we have analyzed in detail the flaws of the distributed systems, common security threats, common scenarios at which the system faces risk, and the best possible solutions to secure the distributed system. Globus security mechanism is discussed in detail as how the authentication can be done and how message confidentiality and integrity can be achieved. Every idea that radiates all sorts of solutions to this matter is verified and analyzed. This paper provides necessary knowledge to help one understand the meaning of a secure system, the security policies, and the security mechanism.

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Vaidya, C., Rangari, S., Walke, T., Phadnis, T., Deshmukh, Y., and Kunte, Y. (2020). Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction. i-manager's Journal on Computer Science, 8(3), 1-11. https://doi.org/10.26634/jcom.8.3.18048

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A Survey of Security Threats in Distributed Operating System

Sumra Khan * , Syed Muhammad Waqas **, Arbaz Ahmed***

*-*** Department of Computer Science, Bahria University, Karachi, Pakistan.

Khan, S., Waqas, S. M., and Ahmed, A. (2020). A Survey of Security Threats in Distributed Operating System. i-manager's Journal on Computer Science, 8(3), 44-60. https://doi.org/10.26634/jcom.8.3.18262

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Chandu Vaidya * , Sajan Rangari , Tanmay Walke *, Tanmay Phadnis **, Yash Deshmukh *, Yash Kunte ****

Samatha Juluri* , Voladri Prashanth Reddy , Dhanunjay Sagar *, Gattu Sai Koundinya ****

Raghul Karthik N.* , Rathina Karthi T. , Kumaresan G. *, Vishal Aakash A. ****

Sumra Khan * , Syed Muhammad Waqas , Arbaz Ahmed*