iosr-JCE   Volume-24 ~ Issue-1 ~ Jan. - Feb. 2022

Abstract: MANET, or mobile ad hoc network, is concerned with the network's boneless structure, which is widely used in hazardous environments. MANET is concerned with self-configuring nodes with router-like capabilities. This property of nodes may result in an unjust decision when it comes to reaching the destination. Because the topology of a MANET differs from case to case, it is difficult to select the optimal method for establishing good communication while minimising data loss and expense. We presented a fair routing technique that takes into account load overhead concerns......

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Abstract: Data mining is a procedure used to evaluate and find the hidden knowledge of a database. It is applicable in various sectors such as weather forecast, hospitals, business industries and many more. Currently, education data mining is the most useful application used to analyze data related to student performance, course outlines, and faculty performance. This paper describes different classification techniques by using large and small datasets. These two datasets are dataset examples used through repository sites. Several instances depend upon these sites. These data sets are applied on Naive Bayes type to show that it is the first-class classifier from small and big statistics sets. This paper offers the observe and evaluation of numerous methodologies used for prediction. Based on observation, Naïve Bayes is more appropriate for small datasets based at the assessment executed on this paper the use of numerous methodologies pushed through the RapidMiner device whilst equating precision, consider and accuracy.

Key Word: Naïve Bayes; Rapid Miner.

[1] A. Singh and R. Sathyaraj, "A Comparison Between Classification Algorithms on Different Datasets Methodologies using Rapidminer," Int. J. Adv. Res. Comput. Commun. Eng., vol. 5, no. 5, pp. 560–563, 2016, doi: 10.17148/IJARCCE.2016.55140.
[2] A. Kori, "Comparative Study of Data Classifiers Using Rapidminer," Internatio nal J. Eng. Dev. Res., vol. 5, no. 2, pp. 2321–9939, 2017.
[3] A. S.-M. Al-Ghamdi and F. Saleem, "in Building and Implementation of Naive Bayes Classification Model: A Domain of Educational Data Mining BUILDING AND IMPLEMENTATION OF NAIVE BAYES CLASSIFICATION MODEL: A DOMAIN OF EDUCATIONAL DATA MINING," Int. J. Adv. Electron. Comput. Sci., no. 6, pp. 2394–2835, 2019, [Online]. Available: http://iraj.
[4] A. Hamzah, "Klasifikasi Teks Dengan Naïve Bayes Classifier (NBC) Untuk Pengelompokan Teks Berita Dan Abstract Akademis," Pros. Semin. Nas. Apl. Sains Teknol. Periode III, no. 2011, pp. 269–277, 2012, doi: 1979-911X.
[5] R. Hossain, R. Ibrahim, R. Binti, D. Zain, and A. M. Khaidzir, "Experimental Study of Support Vector Machines and Naïve Bayes Classifier on Automated Subject Area Classification," J. Inf. Syst. Res. Innov., vol. 11, no. December, pp. 7–13, 2017.

Abstract: Technology-dependent human evolution is an irreversible process. Information and communication technology (ICT) has encapsulated every stream of knowledge by this day, in 2021. Existence of life cannot be imagined without ICT. All spectrums of the ICT are in turn dependant on the semiconductor microprocessor chip, popularly known as microchip or simply chip. The demand of the microprocessor chips was pegged at INR 32.0 Lakh Crores globally and estimated at INR 5.20 Lakh Crores in India by 2015. India depends on the import for 100% of the required number of microprocessors. Indian academia has failed to recognize the critical significance of microprocessor manufacturing knowledge. In absence of indigenous manufacturing of microchips, the country runs the risk of interruptions in the supply due to global shortage or denial of supply due to geo-political competition or conflicts........

Key words: Semiconductor, Microprocessor, Microprocessor manufacturing, Microchip, Chipset, E-waste, Re-use of microchips, Lithography, Atmnirbhar.

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Abstract: One of the causes of the gradual decline of the Nigerian Educational System can be attributed to the inadequacies and flaws in universities admission process. In this paper, we seek to address this problem by predicting the first-year performances of prospective students using Fuzzy Logic based on prior academic achievements presented during admission process. A range of conventional secondary school subjects were used as the input variables in a Mamdani Model. MATLAB fuzzy logic toolbox was used to model the fuzzy rules logic in this study with computer science course (CSC) criteria in the x axis, General studies course (GST) criteria is in y axis, and solution criteria Performance (P) is in z axis. The results show that a student with a model output of CSC=0.857, MTH=0.796, PHY=0.864, GST=0.638, CHM=0.489 model output P=0.446. Output P=0.446 means that the student will most likely perform above the 95th percentile of the Grade Point Average (GPA). The design was successfully tested and implemented.

Key Word: Fuzzy Logic, Student Performance, Fuzzy Inference.

[1]. Abruzzi, Kristen J.; Lenis, Cristina; Romero, Yansi V.; Maser, Kevin J.; Morote, Elsa-Sofia (2016). "Does Participation in Extracurricular Activities Impact Student Achievement?". Journal for Leadership and Instruction. 15 n1: 21–26.
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Abstract:Background: The k-means algorithm is to divides the data into clusters based on the closest distance by using the Euclidean distance formula. K-means is often used because the resulting approach is easy to implement, but has the disadvantage that the central point depends on the choice of K, resulting in a decrease in the speed and quality of a cluster. Methodology: The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) cycle using 6 phases. The purpose of this research is to automate the selection of numbering on K-means so as to further increase the speed and quality of a cluster.
Discussion: The algorithm used is the K-means algorithm and Particle Swarm Optimization (PSO) which is validated using the K-nn algorithm because if it is calculated.....

Key Word: Clustering; Fishery Production Volume; K-means Algorithm; PSO Algorithm

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[2]. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of data clusters via the gap statistic. In Journal of the Royal Statistical Society: Series B (Vol. 63, Issue Part 2, pp. 411–423).
[3]. Wang, J. (2010). Consistent selection of the number of clusters via crossvalidation. Biometrika, 97(4), 893–904. https://doi.org/10.1093/biomet/asq061
[4]. Saha, J., & Mukherjee, J. (2021). CNAK: Cluster number assisted K-means. Pattern Recognition, 110, 107625. https://doi.org/10.1016/j.patcog.2020.107625
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Abstract: The progress of communication systems has allowed us to think beyond traditional communication systems, and the scene has been set for thought-oriented communication systems. Thousands of thoughts are formed and then evaporate in a short period of time, yet certain notable concepts remain and we carry on with our daily routines. EEG has advanced to the point that it is now possible to see the activity in the human brain in a non-invasive manner. The approach for emotion identification utilizing EEG data recorded and processed on smart devices is presented in this study. The results demonstrate the use of a computational neural network to recognize emotions from EEG data. It was discovered that the correct categorization rate was 90.17 percent.

Keywords: Emotions, EEG, FAST Independent Component Analysis, Computational Neural Network

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[2]. Rutkowski, Tomasz M., and Hiromu Mori. "Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users." Journal of Neuroscience Methods 244 (2015): 45-51.
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Abstract:The major concern of companies is the good management of information with the help of devices and computer software that meet the specific needs of each company and a mutual that functions in the form of a tontine or having some similarity with micro-finances granting credit to its members. It should be noted that the credit offered by banks, micro-finance cooperatives, mutuals, etc. allows us to finance our activities, our projects, our households, etc. in need of financing. The latter increases the human activities.........

Keywords: management, mutual, cooperative and credit.

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Abstract: Cloud computing is a strategy for extending the breaking point or add confines honestly without placing assets into new system, getting ready new workforce, or allowing new programming. As information exchange expects a huge part in the continuous life, information security ends up being more essential. This paper relies upon the security issues of cloud computing and way to deal with beat the data security issue. Going prior to secluding the security issues, the meaning of cloud computing and brief discussion to under cloud computing is presented.
Cloud Computing is one of the most astounding headway advancement of late.........

Keywords: Security, Cloud, Computing

[1]. Mohis M and Devipriya V S, "An improved approach for Enhancing Public Cloud Data Security through Steganographic Technique," IEEE, pp. 1-5, 2016.
[2]. Kunal V. Raipurkar and Anil V. Deorankar , "Improve Data Security in Cloud Environment by using LDAP and Two Way Encryption Algorithm," IEEE, pp. 1-4, 2016.
[3]. V.Swath, K.Sudha, R.Aruna, C.Sangeetha and R.Janani , "Providing Advanced Security Mechanism for Scalable Data Sharing In Cloud Storage," IEEE, pp. 1-6, 2016.
[4]. Shivangi Sengar and Rajesh Kumar Chakrawarti , "Implementation of PDS System with Improved Security and Transparency under Cloud Environment," IEEE, pp. 1-6, 2016.
[5]. Mohis M and Devipriya V S, "An improved approach for Enhancing Public Cloud Data Security through Steganographic Technique," IEEE, 2016.

Abstract: Mobile data is one of the most valuable sources of data available today. It contains a wealth of information about our behavior, preferences, and habits. However, mobile data can also be very messy and difficult to work with. This is because it is often collected from a variety of sources, in different formats, and with different levels of quality. Data preprocessing is the process of preparing raw data for analysis. It is a crucial step in any data science project, as it can have a significant impact on the quality and accuracy of the results. Data preprocessing is a complex and time-consuming task, but it is essential for ensuring that the data is in a format that can be used by machine learning algorithms.........

Keywords: AI, tools, pre-processing, mobile

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