Abstract

nThe rapid expansion of big data has posed substantial difficulties for conventional computing systems. As a result, cluster computing has grown to be a potent method for effective large data processing. Cluster computing involves multiple interconnected nodes functioning as a unified system, pooling together their processing, storage, and memory resources. These nodes are typically connected through high-speed networks such as ethernet or InfiniBand, facilitating efficient data sharing and communication among them. Big data has made cluster computing frameworks like Apache Hadoop and Apache Spark very popular. These frameworks provide accessible tools and libraries that make creating and running parallel computing tasks on a cluster easier. Additionally, they have fault tolerance methods to ensure system resilience in the event of node failures, protecting data integrity and allowing computation to continue without interruption. By leveraging interconnected computers working in unison, cluster computing enables parallel processing, leading to faster and more scalable data analysis. This paper examines the effects of cluster computing on both big data analysis and network topology.

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1. Buyya R, Vecchiola C, Selvi ST. Mastering Cloud Computing: Foundations and Applications Programming. Cambridge, MA: Morgan Kaufmann; 2013.
2. Mayer-Schönberger V, Cukier K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt; 2013.
3. Medhi D, Ramasamy K. Network Routing: Algorithms, Protocols, and Architectures. Cambridge, MA: Morgan Kaufmann; 2017.
4. Ridge D, Becker D, Merkey P, Sterling T. Beowulf: harnessing the power of parallelism in a pile-of-PCs. In: 1997 IEEE Aerospace Conference, Snowmass, CO, USA, February 13, 1997. Volume 2, pp. 79–91.
5. Lin J, Dyer C. Data-intensive text processing with MapReduce. In: Hirst G, series editor. Synthesis Lectures on Human Language Technologies #7. Kentfield, CA: Morgan & Claypool Publishers;
6. Sa-Ngasoongsong A, Kunthong J, Sarangan V, Cai X, Bukkapatnam ST. A low-cost, portable, high-throughput wireless sensor system for phonocardiography applications. Sensors. 2012; 12 (8): 10851–10870.
7. Miller TC, Stirlen C, Nemeth E. satool – a system administrator’s cockpit, an implementation. In: Seventh System Administration Conference: LISA 1993, Monterey, CA, USA, November 5, 1993. pp. 119–130.
8. MarketWide Research. Cluster computing market analysis -– industry size, share, research report, insights, covid-19 impact, statistics, trends, growth and forecast 2023-2030. [Online]. 2023. MarkWide Research. 2023. Available at https://markwideresearch.com/cluster-computing-market/
9. Saturn Cloud. Hadoop how to unit test filesystem. [Online]. 2023. Available at https://saturncloud.io/blog/hadoop-how-to-unit-test-filesystem/
10. NAKIVO Team. High availability vs fault tolerance vs disaster recovery. [Online]. NAKIVO Team. 2018. Available at https://www.nakivo.com/blog/disaster-recovery-vs-high-availability-vs-fault-tolerance/
11. Mosley D. Network topology definitions – designing infrastructure Windows Server 2003. [Online]. 2023. Windows Server Brain. Available at https://www.serverbrain.org/designing-infrastructure-2003/network-topology-definitions.html

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Abstract

nFraud has posed a significant challenge for merchants, especially in the online business sector, over the course of many years. This is primarily due to the advancements in technology that have made credit card transactions a common method of payment. Credit card fraud refers to the unauthorized use of a credit card by an individual for personal purposes, without the owner’s consent and with no intention of paying for the incurred expenses or engaging in deceptive activities to gain financial advantage. Given the efforts made by fraudsters to disguise their transactions as legitimate, this study introduces an artificial neural network model powered by a machine learning algorithm to identify and detect fraudulent activities in credit card transactions. The researchers effectively filtered and cleansed the dataset sourced from Kaggle machine learning repository selection techniques. The experiment was set up on a 64-bit Windows OS on an Intel (R) Core (TM) i5-3530 QM CPU @ 2.40 GHZ. Python 3.10 via Anaconda environment using Jupyter notebook was used as the integrated development environment. Dataset exploration, reading, scaling and performance evaluation were done successfully. The study result found prediction accuracy of 0.9184, which is equivalent to 92% at step 716 with 4.6 ms conducted per step and also loss metric based on binary entropy of 2.0%. The study recommends future research and advancement in artificial neural network by hybridizing deep neutral network and Relu neural network for multi-perception optimized performance.

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1. Bhatla TP, Prabhu V, Dua A. Understanding credit card frauds. Cards Business Rev. 2003; 1 (6): 1–15.
2. Tsai CF. Combining cluster analysis with classifier ensembles to predict financial distress. Inform Fusion. 2014; 16: 46–58.
3. Kang F, Cheng D, Tu Y, Zhang L. Credit card fraud detection using convolutional neural networks. In: Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D, editors. Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science, Volume 9949. Cham, Switzerland: Springer; 2016. pp. 483–490. doi: 10.1007/978-3-319-46675-053.
4. Deepika S, Senthil S. Credit card fraud detection using moth-flame earthworm optimization algorithm-based deep belief neural network Int J Electron Security Digital Forensics. 2021; 14 (1): 53–75.
5. Esenogho E, Mienye ID, Swart TG, Aruleba K, Obaido G. A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access. 2022; 10: 16400–16407. doi: 10.1109/ACCESS.2022.3148298.
6. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC. Data mining for credit card fraud: a comparative study. Decis Support Syst. 2011; 50 (3): 602–613.
7. Randhawa K, Loo CK, Seera M, Lim CP, Nandi AK. Credit card fraud detection using AdaBoost. IEEE Access. 2018; 6: 14277–14284. doi: 10.1109/ACCESS.2018.2806420.
8. John OA, Adebayo OA, Samuel AO. Credit card fraud detection using machine learning techniques: a comparative analysis. Int J Soft Comput Eng. 2017; 1: 32–38.
9. Prusti D, Rath SK. Web service-based credit card fraud detection by applying machine learning techniques. In: Proceedings of the TENCON 2019: -IEEE Region 10 Conference (TENCON), October 17–20, 2019, Kochi, India, pp. 492–497. doi: 10.1109/TEN-CON.2019.8929372.
10. Faraji Z. A review of machine learning applications for credit card fraud detection with a case study. SEISENSE J Manage. 2022; 5 (1): 49–59. doi: /10.33215/sjom.v5i1.770.
11. Bommala H, Basha RM, Rajarao B, Sangeetha K. An innovative model-based approach for credit card fraud detection using K-nearest. In: Reddy AB, Kiranmayee B, Mukkamala RR, Srujan Raju K, editors. International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Singapore: Springer; 2022. pp. 199–206. doi: 10.1007/978-981-16-7389-4_19.

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Abstract

nIn recent years, there has been a growing interest and emphasis on agricultural land preparation and its implementation among researchers, primarily due to various factors. These factors include an increased focus within the research community, a rising demand for agricultural land, and the significance of assessing soil health for ensuring robust crop production. Picture request is one such philosophy for soil and land prosperity examination. It is a staggering measure having the effects of various dimensions. This paper has suggested an investigation into the stream, analyzing the problems it addresses, as well as its future possibilities. The emphasis is focused on the intelligent examination of various advanced and successful gathering frameworks and methodology. Here, various components have been taking into account and these techniques have been directed to work on the accuracy of the portrayal. Suitable use of the features of remotely recognized data and picking the best sensible classifier are by and large huge for working on the accuracy of the data collected. The data-based game plan or non-parametric classifier like brain network has procured pervasiveness for multisource data gathering lately. Nevertheless, there is at this point the degree of extra investigation, to decrease weaknesses in the improvement of accuracy of the image gathering instruments. Support vector machine calculation is utilized to suggest the harvests in light of the soil conditions. Within this project, we strongly advocate for the adoption of the K nearest neighbor model by industries as well. We have created a skilled dataset for industries. We have worked on five regions, namely Konkan, Marathwada, Vidarbha, Nashik, and Pune.

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1. Vlachopoulos O, Leblon B, Wang J, Haddadi A, LaRocque A, Patterson G. Evaluation of crop health status with UAS multispectral imagery. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2022; 15: 297–308.
2. Rao MS, Singh A, Subba Reddy NV, Acharya DU. Crop prediction using machine learning. J Phys Conf Series. 2021; 2161: 012033. doi: 10.1088/1742-6596/2161/1/012033.
3. Nejad SMM, Abbasi-Moghadam D, Sharifi A, Farmonov N, Amankulova K, Lászlź M. Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2023; 16: 254–266. doi: 10.1109/JSTARS.2022.3223423.
4. Usha Devi R, Sheela Selvakumari NA. Crop prediction and mapping using soil features with different machine learning techniques. In: Proceedings of the International Conference on Innovative Computing and Communication (ICICC) 2022, February 19–120, New Delhi, India, 2022. doi: 10.2139/ssrn.4097213.
5. Hina F, Hasan MT. Agriculture crop yield prediction using machine learning. Int J Res Appl Sci Eng Technol. 2022; 10 (IV): 910–915.
6. Rajeswari SR, Khunteta P, Kumar S, Singh AR, Pandey V. Smart farming prediction using machine learning. Int J Innov Technol Exploring Eng. 2019; 8 (7): 190–194.
7. Puno JCV, Bedruz RAR, Brillantes AKM, Vicerra RRP, Bandala AA, Dadios EP. Soil nutrient detection using genetic algorithm. In: 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines, November 29–December 1, 2019. pp. 1–6. doi: 10.1109/HNICEM48295.2019.9072689.
8. Reshma R, Sathiyavathi V, Sindhu T, Selvakumar K, SaiRamesh L. IoT based classification techniques for soil content analysis and crop yield prediction. In: Proceedings of the 2020 Fourth International Conference on I- SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), October 7–9, 2020. Palladam, India. pp. 156–160.
9. Jain S, Ramesh D. Machine learning convergence for weather based crop selection. In: 2020 IEEE International Students’ Conference on Electrical,Electronics and Computer Science (SCEECS), Bhopal, India, February 22–23, 2020. pp. 1–6. doi: 10.1109/SCEECS48394.2020.75.
10. Mariammal G, Suruliandi A, Raja SP, Poongothai E. Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers. IEEE Transac Comput Soc Syst. 2021; 8 (5): 1132–1142. doi: 10.1109/TCSS.2021.3074534.
11. Mahendra N, Vishwakarma D, Nischitha K, Ashwini, Manjuraju MR. Crop prediction using machine learning approaches. Int J Eng Res Technol. 2020; 9 (8): 23–26.
12. Vadalia D, Vaity M, Tawate K, Kapse D. Real time soil fertility analyzer and crop prediction. Int Res J Eng Technol. 2017; 4 ()3: 1497–1499.
13. Devi MPK, Anthiyur U, Shenbagavadivu MS. Enhanced crop yield prediction and soil data analysis using data mining. Int J Modern Computer Sci. 2016; 4(6).

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Abstract

nVirtual try-on of clothes is gaining popularity thanks to its commercial potential. It can be used for intelligent recommendation or online purchasing to reduce the selection to a few designs and sizes. Through this project we aim to create a mechanism that enables users to see themselves wearing virtual clothes while looking at a screen display, without taking off their actual clothes. We aim to stimulate the selected clothes on the captured image of the users, and they can see virtual clothes fitting on the image provided. The main drawback in the implementation of the previous works is that the change in pose of users may distort the clothes, which may lead to inaccurate results. In this project, we have implemented an image-based virtual cloth try-on model that can retain the texture and patterns of the clothing the user wishes to buy. This can be used to improve the experience of clients on e-commerce websites and other clothing industry portals and outlets

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1. Eisert P, Hilsmann A. Realistic virtual try-on of clothes using real-time augmented reality methods. E-Letter. 2011; 6 (8): 37–40.
2. Joshi P, Desale V, Jadhav A, Pawar S. Survey on context-driven image-based virtual try-on network. Int Res J Modern Eng Technol Sci. 2023; 5 (1): 1409–1413.
3. Spanlang B, Vassilev T, Buxton BF. Compositing photographs with virtual clothes for design. In: CompSysTech 2004: Proceedings of the 5th International Conference on Computer Systems and Technologies, Rousse, Bulgaria, June 17–18, 2004. pp. 1–6.
4. Duchon J. Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: Constructive Theory of Functions of Several Variables: Proceedings of a Conference Held at Oberwolfach, Germany, April 25–May 1, 1976. Berlin, Germany: Springer; 1977. pp. 85–100.
5. Güler RA, Neverova N, Kokkinos I. DensePose: Dense human pose estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, UT, USA, June 18–23, 2018. pp. 7297–7306.
6. Dong X, Zhao F, Xie Z, Zhang X, Du DK, Zheng M, Long X, Liang X, Yang J. Dressing in the wild by watching dance videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022, New Orleans, LA, USA, June 18–24, 2022. pp. 3470–3479.
7. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV, USA, June 27–30, 2016. pp. 770–778.
8. Han X, Wu Z, Wu Z, Yu R, Davis LS. Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, UT, USA, June 18–23, 2018. pp. 7543–7552.
9. Padha ES, Anthal MT, Sharma MR, Singh MG, Wani MM. Enhancement in thermos flask: a review. Global Res Dev J Eng. 2017; 3 (1): 33–37.
10. Landers-Ramos RQ, Dondero K, Nelson C, Ranadive SM, Prior SJ, Addison O. Muscle thickness and inflammation during a 50 km ultramarathon in recreational runners. PLoS One. 2022; 17 (9): e0273510.
11. Yuan M, Khan IR, Farbiz F, Yao S, Niswar A, Foo MH. A mixed reality virtual clothes try-on system. IEEE Trans Multimedia. 2013; 15 (8): 1958–1968.

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Abstract

nThis research paper presents a highly efficient algorithm for substring search within a given string, achieving a remarkable time complexity of O(n). The proposed algorithm utilizes a two-pointer approach to compare the given string with the targeted substring. By employing string concatenation, the algorithm dynamically constructs a resultant substring during the matching process. Upon completion of character matching, the algorithm compares the resultant substring with the targeted substring and returns the index position when a match is found. The main advantage of this algorithm is to search through the main string in linear time, outperforming alternative algorithms with higher time complexities such as O(n^2) or O(m*n). The practical applications of this algorithm are diverse, spanning text processing, pattern matching, and data analysis. The efficiency and effectiveness of the algorithm make it a valuable tool in various domains where rapid and accurate substring search is required. By introducing this algorithm, the research paper offers a substantial contribution to the field of string processing, addressing the need for efficient substring search techniques. The experimental evaluation of the algorithm demonstrates its superior performance compared to existing approaches, highlighting its potential for enhancing computational tasks that involve string manipulation. The presented algorithm opens new possibilities for improving efficiency and scalability in applications that rely on substring search operations, contributing to advancements in text mining, information retrieval, and data analytics.

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1. Aho AV, Corasick MJ. Fast pattern matching: an aid to bibliographic search. Commun ACM. 1975; 18 (6): 333–340.
2. Damiani A, Masciocchi C, Lenkowicz J, Capocchiano ND, Boldrini L, Tagliaferri L, Cesario A, Sergi P, Marchetti A, Luraschi A, Patarnello S. Building an artificial intelligence laboratory based on real world data: the experience of Gemelli generator. Front Computer Sci. 2021; ;3: 768266.
3. Rahim R, Ahmar AS, Ardyanti AP, Nofriansyah D. Visual approach of searching process using Boyer-Moore algorithm. J Phys Conf Series. 2017; 930 (1), 012001.
4. Knuth DE, Morris JH Jr, Pratt VR. Fast pattern matching in strings. SIAM J Comput. 1977; 6 (2): 323–350.
5. Namjoshi K, Narlikar G. Robust and fast pattern matching for intrusion detection. In: 2010 Proceedings IEEE INFOCOM, Sandiego, CA, USA, March 14–19, 2010. pp. 1– doi: 10.1109/INFCOM.2010.5462149.
6. Gurung D, Chakraborty UK, Sharma P. Intelligent predictive string search algorithm. Procedia Computer Sci. 2016; 79: 161–
7. Carmosino ML, Gao J, Impagliazzo R, Mihajlin I, Paturi R, Schneider S. Nondeterministic extensions of the strong exponential time hypothesis and consequences for non-reducibility. In: Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, Cambridge, MA, USA, January 14–17, 2016. pp. 261–
8. March SD, Jones AH, Campbell JC, Bank SR. Multistep staircase avalanche photodiodes with extremely low noise and deterministic amplification. Nat Photonics. 2021; 15 (6): 468–
9. Hume A, Sunday D. Fast string searching. Softw Pract Experience. 1991; 21 (11): 1221–
10. Hooimeijer P, Veanes M. An evaluation of automata algorithms for string analysis. In: Verification, Model Checking, and Abstract Interpretation: 12th International Conference, VMCAI 2011, Austin, TX, USA, January 23–25, 2011. Berlin, Germany: Springer; 2011. pp. 248–

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