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Buhari, Bello A.
- Adoption of Cloud Computing by IT based Small and Medium Scale Enterprises in Northwestern Nigeria
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Authors
Affiliations
1 Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
2 MIT Student, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
3 Management Information System Usmanu Danfodiyo University, Sokoto, NG
1 Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
2 MIT Student, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
3 Management Information System Usmanu Danfodiyo University, Sokoto, NG
Source
International Journal of Advanced Networking and Applications, Vol 13, No 5 (2022), Pagination: 5119-5127Abstract
This research has taken a Quantitative, interpretive and cross-sectional designs in the form of a self-administered questionnaire through survey. The aim is to investigate the adoption of Cloud Computing by IT based small and medium scales enterprises in Northwestern Nigeria. About one hundred and fifty (150) questionnaires were distributed among seven states in the North-Western Nigeria and average of one hundred and nine (109) was responded. The result of the survey shown that most of the IT professional in these SMEs are cloud provider’s end users, continue to use cloud provider in the future, is part of their strategic effort, recommended Cloud provider to others and they are very satisfied with the cloud providers. Challenges that are preventing them from getting the maximum value out of cloud providers are lack of encouragement, poor training, application is missing and lack of executive sponsorship.Keywords
Cloud Computing, IT, SMEs. Adoption, Northwestern Nigeria, Nigeria.References
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- Ibrahim, M. A. (2015). Exploring the Feasibility of Adopting Cloud Computing in Computer Center Taiz University. International Journal of Advanced Networking and Applications, 6(4), 2359.
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- Bashir, S. A., Adebayo, O. S., Abdulsalam, S. O., Sadiku, J. S., & Mabayoje, M. A. (2015). A Survey of Cloud Computing Awareness, Security Implication and Adoption in Nigeria IT Based Enterprises.
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- An Empirical Study Based on SMEs Employees Perceptions. Sustainability, 12(12), 4960.
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- Investigation of the Challenges of Cloud-Based ERP Adoption in Pakistani SMEs. Scientific Programming, 2021.
- Design of a Secure Virtual File Storage System on Cloud using Hybrid Cryptography
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Authors
Affiliations
1 Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
2 Undergraduate Student, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
3 Management Information System Usmanu Danfodiyo University, Sokoto, NG
1 Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
2 Undergraduate Student, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, NG
3 Management Information System Usmanu Danfodiyo University, Sokoto, NG
Source
International Journal of Advanced Networking and Applications, Vol 13, No 5 (2022), Pagination: 5143-5151Abstract
As Security is becoming more and more useful in the field of computing, users would like to be sure of how secure their files are on a system, as security is one of the most crucial fields in networking and file storage. Dependable file storage and access establish several security issues in a cloud computing. This research designed and implemented virtual secure file storage system on cloud using hybrid cryptography. The cryptography method used for file encryption and decryption is AES and SHA-2 hash function. It is implemented using Cloud APIs with REST calls or client libraries in PHP. The system interfaces were developed using HTML, CSS and JAVASCRIPT. Back end development was done using PHP, MYSQL and GCP Cloud Storage Library then the file encryption and decryption was achieved through PHP classes which includes open_ssl_file_encryption and decryption (AES) and also MCRYPT function. The proposed virtual system is also compared with some latest related works.Keywords
Cloud Computing, Secure file storage, hybrid cryptography, AES, SHA-2, Google Cloud Platform.References
- Bindu, B. S., & Yadaiah, B. (2011). Secure data storage in cloud computing. International Journal of Research in Computer Science, 1(1), 63-73.
- Rajathi, A., & Saravanan, N. (2013). A survey on secure storage in cloud computing. Indian Journal of Science and technology, 6(4), 4396-4401.
- Karati, A., Amin, R., Mohit, P., Sureshkumar, V., & Biswas, G. P. (2021). Design of a secure file storage and access protocol for cloud-enabled Internet of Things environment. Computers & Electrical Engineering, 94, 107298.
- Suresh, S. R. (2021). An Electronic Digital Library Using Integrated Security Methods and Cloud Storages. International Journal of Advanced Networking and Applications, 13(1), 4839-4844.
- Olanrewaju, O., Oluwatoyin, A. A., & Mary, O. T. C. (2020). Secure Online Electronic Civil Registration Using Cloud Computing: A Conceptual Framework. International Journal of Advanced Networking and Applications, 11(5), 4418-4422.
- Popa, R. A., Lorch, J. R., Molnar, D., Wang, H. J., & Zhuang, L. (2011, June). Enabling Security in Cloud Storage SLAs with Cloud Proof. In USENIX Annual Technical Conference (Vol. 242, pp. 355-368).
- Kute, S., & Javheri, S. B. (2018, August). Implementation of Secure File Storage on Cloud with Owner-Defined Attributes for Encryption. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-6). IEEE.
- Swarna, C., & Eastaff, M. S. (2018). Secure File Storage in Cloud Computing Using Hybrid Cryptography Algorithm. Iaetsd Journal for Advanced Research in Applied Science.
- Poduval, A., Doke, A., Nemade, H., & Nikam, R. (2019). Secure File Storage on Cloud using Hybrid Cryptography. International Journal of Computer Science and Engineering, 7.
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- Seth, B., Dalal, S., Jaglan, V., Le, D. N., Mohan, S., & Srivastava, G. (2020). Integrating encryption techniques for secure data storage in the cloud. Transactions on Emerging Telecommunications Technologies, e4108.
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- Abdel-Kader, R. F., El-Sherif, S. H., & Rizk, R. Y. (2020). Efficient two-stage cryptography scheme for secure distributed data storage in cloud computing. International Journal of Electrical & Computer Engineering (2088-8708), 10(3).
- Rashmi, R. P., Gandhi, Y., Sarmalkar, V., Pund, P., & Khetani, V. (2020, October). RDPC: Secure Cloud Storage with Deduplication Technique. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp.1280-1283). IEEE.
- Sheeja, R., Bibin, C., Krishnan, P. R., Nishanth, R., Gopinath, S., & Ashok, K. G. (2020, August). Secure File Sharing System in Cloud Using AES and Time Stamping Algorithms. In IOP Conference Series: Materials Science and Engineering (Vol. 906, No. 1, p. 012023). IOP Publishing.
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- Chinnasamy, P., Padmavathi, S., Swathy, R., & Rakesh, S. (2021). Efficient Data Security Using Hybrid Cryptography on Cloud Computing. In Inventive Communication and Computational Technologies (pp. 537-547). Springer, Singapore.
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- On the Analysis of Some Machine Learning Algorithms for the Prediction of Diabetes
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Authors
Affiliations
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
2 Department of Computer Science, Waziri Ummaru Federal Polytechnic, Birnin-Kebbi, NG
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
2 Department of Computer Science, Waziri Ummaru Federal Polytechnic, Birnin-Kebbi, NG
Source
International Journal of Advanced Networking and Applications, Vol 14, No 1 (2022), Pagination: 5294-5299Abstract
Diabetes or Diabetes Mellitus (DM) is noxious diseases in the world. Diabetes is caused by obesity or high blood glucose level, lack of exercise and so forth. It can be manage if it’s detected at early state. Machine learning is the construction of computer system or program that can adapt and learn from their experience. PIMA dataset is used in this research works. The dataset contains some 9 attributes of 768 patients. There are different kinds of machine learning algorithms but in this research works we choose three algorithms which are under supervised learning. The algorithms are Logistic regression, Decision tree and Random forest. Each of these algorithms model were trained and tested. We later use some measure to compare and analyze the performance of the machine learning algorithms. The performance measures used are Accuracy, F-measure, Recall and Precision. Logistic Regression has the highest accuracy score which is 77%, also have the highest precision score 0.77 and have the highest f-measure 0.64. Decision Tree has the highest recall score 0.58.Keywords
Diabetes, Machine Learning, Logistic Regression, Decision Tree, Random Forest.References
- Deeraj Shetty, Kishor Rit, Sohail Shaikh, Nikita Patil, "Diabetes Disease Prediction Using Data Mining ".International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017.
- Tejas N. Joshi, Prof. Pramila M. Chawan, "Diabetes Prediction Using Machine Learning Techniques". Int. Journal of Engineering Research and Application, Vol. 8, Issue 1, (Part -II) January 2018, pp.-09-13.
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- Nonso Nnamoko, Abir Hussain, David England, "Predicting Diabetes Onset: an Ensemble Supervised Learning Approach ". IEEE Congress on Evolutionary Computation (CEC), 2018.
- Mitushi Soni, ‘Diabetes Prediction using Machine Learning Techniques’, International Journal of Engineering Research & Technology, Vol. 9, Issue 9, September 2020.