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The location of the mobile nodes in the MANET IoT changes continuously that`s why the communication among them is difficult. The different devices or nodes in the Internet of Things (IoT) connect with each other over the internet or convey information to each other if they are immediately in range. The existence of an attacker is a difficult issue in a network since it lowers routing performance and has an impact on node battery life. Secure routing is critical to the adoption and deployment of many IoT applications. Sybil attacks may be destructive to MANET IoT and constitute a significant problem for building effective IoT security solutions. In this dissertation, proposes the Hybrid trust based enhanced security technique to protect the MANET IoT network against Sybil Attack. The preceding Sec Trust method is recommended as a dependable approach in IoT to safeguard communication from Sybil attack. The proposed system also decreases energy usage, which increases network life time. The performance of both the schemes is measured in different node density situations, but Hybrid trust performs better. The Sec trust system is dependable and secure, but it is inefficient in routing between the source and destination. The efficient routing technique decreases network overhead, which reduces packet flooding and, as a result, improves routing efficiency. The Hybrid trust method enhances routing reliability by consuming the energy consumption of mobile nodes in an MANET IoT network.

IoT-MANET, Nodes, Hybrid Trust, Sec trust, Routing, Sybil Attacker

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Prince Kumar, Rachana kamble, "A Deep Study of Hybrid Trust Built To Improve Security Technique against Sybil Attack in MANET Based IoT Network," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.1-8, 2021.

Text classification is the one of the primary tasks in Natural Language Processing (NLP). Key phrase extraction is the fundamental component that aids the mapping of documents to a set of emblematic phrases. For example, a category that includes IT documents can be described as “Information and Computer” or “Information and Technology”. If a text document includes keywords such as “issue” and “order”, then it belongs to “Issue Category”. Multiple pre-trained and deep learning approaches are available now-a-days for semantic analysis. Word embeddings are predominant technique that provides light to find the semantic similarity between tokens/phrases using word vectors. The most widely used word embeddings are GloVe, Word2vec, BERT etc. Experimental results show that the strategy produced by this study have more precision and simplicity than that of other methods.

Document Categorization, Keywords Extraction, Concept Learning, Multi-class Probabilistic Classification, Content Mining

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[3] R Shrihari C, Desai A, “A review on knowledge discovery using text classification techniques in text mining,” International Journal of Computer Applications (0975 – 8887), vol. 111 – No 6, February 2015.
[4] Dang, S., & Ahmad, P.H ,”A Review of Text Mining Techniques Associated with Various Application Areas”, International Journal of Science and Research (IJSR), Vol.4, No.2, pp.2461-2466,2015.
[5] Mehmet Fatih KARACA and Safak BAYIR "Examining the Impact of Feature Selection Methods on Text Classification" Published by : (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 12 , 2017.
[6] R. Janani1, S. Vijayarani "Text Classification: A Comparative Analysis of Word Embedding Algorithms", International Journal of Computer Sciences and Engineering, Vol.7, Issue-4, April 2019.
[7] Riya Tyagi "Review of Extraction and Classification of Key-Phrases in Scientific Publications using CRF and WEDP" Published by : International Journal of Engineering Research & Technology, Volume 09, Paper ID : IJERTV9IS060480, 2020.
[8] Rafly Indra and Abba Suganda Girsang "Classification of User Comment Using Word2vec and Deep Learning" Published by : Advances in Science, Technology and Engineering Systems Journal, Volume 9 No.1 , Page 643– 648 , 2021.
[9] S. Sreedhar, S. Ahmed, P. Flora, LS Hemanth, J. Aishwarya, R. Naik "An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique",Published by : International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8 Issue-2S10. January 2021.

Ananya Srivastava, Lavanya Gunasekar, Bagya Lakshmi V., Navneeth Devaraj, "Unsupervised Context-Based Probabilistic Text Classification," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.9-14, 2021.

The need for excellent delivery in wireless communication and the demand for high data rate transmission have resulted in the investigation of propagation mechanisms for higher-order frequencies, with enormous prospects in increasing data rate with respect to higher bandwidth. Deficiencies in the wireless channel such as poor signal quality, blocked calls, dropped calls, interference problems, etc make it eminent for the development of path loss prediction and estimation models that predict the signal strength of any given terrain. This research is focused on determining the mathematical model that characterizes the propagation environment of a dense urban area in Lagos, Nigeria (Festac town). The RSS measured was used to characterize the environment and a path loss model was developed for both UMTS and LTE in the test environment as ???????? (d) = 77 + 32 ???????????? (????) and ???????? (????????) = 81 + 26 ???????????? (????) respectively. The path loss model developed was tested and its goodness of fit (R^2) was found to be 0,83 for both UMTS and LTE network respectively. This serves as a basis for predicting the path loss of measured data in the environment under research with greater precision and helps in optimizing the overall performance of the wireless mobile network in proffering seamless services in Lagos, Nigeria.

Path-loss, Channel Characterization, LTE, UMTS, Lagos

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Nnebe Scholastica U., Odeh I. Ochim, Nwankwo Vincent I., "Channel Characterization of a Dense UMTS and LTE Network for Improved Services in South-West, Nigeria," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.15-22, 2021.

Pests are organisms that spread diseases as well as causes destruction to the crops. Detection of pests is a must-do in the field of agriculture as growing plants to their fullest requires making the plant free from diseases. Although there are pesticides and insecticides available in the market, proper use of them and selection of them is a must to avoid excessive use or improper use of pesticide and insecticide. In this proposed system, pests are first attracted to a chemical named 1-Octen-3-ol above which flypaper is placed which will trap the small insects after which those insect gets detected using a USB digital microscope endoscope magnifier video camera and YOLO real-time object detection algorithm. The experiment has shown accurate results and might be a useful solution for preventing pests from destroying crops.

Pests, Agriculture, Microscope, Endoscope, Insecticide, Pesticide, YOLO, Deep learning, Image Processing.

[1] D. Gondal, Y. Khan, “Early Pest Detection from Crop using Image Processing and Computational Intelligence”, FAST-NU Research Journal (FRJ), Volume 1, Issue 1, January 2015
[2] https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0475-z
[3] https://assets.researchsquare.com/files/rs242641/v1/9584bc73-4d82-4f48-8474 aea00f7d704f.pdf?c=1631874347
[4] P. Ashok, J. Jayachandran, “Pest Detection and Identification by Applying Color Histogram and Contour Detection by Svm Model”, Volume 8, Issue 3S, February 2019
[5] L. Deng, Y. Wang, “Research on insect pest image detection and recognition based on bio-inspired methods”, Volume 169, Pages 139-148, May 2018
[6] Faithpraise Fina, “AUTOMATIC PLANT PEST DETECTION AND RECOGNITION USING k-MEANS CLUSTERING ALGORITHM AND CORRESPONDENCE FILTERS”, IJABR, Vol 4, Issue 2, 2013, pp 189-199
[7] Jun Lui, “Tomato Disease and Pest Detection based on Improved YOLO V3 CNN”, Frontiers, 16 June 2020
[8] Aparajita Datta, “Classification of Agricultural Pests Using Statistical and Color Feature Extraction and Support Vector Machine”, IJCSE, Volume 7, Issue 1, Page no. 37-41, Jan 2019
[9] D. Sindhu, “Image Processing Technology Application for Early Detection and Classification of Plant Diseases”, IJCSE, Volume 7, Issue 5, Page no. 92-97, May 2019

Harshil Rana, Reema Pandya, "Pest Detection System," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.23-25, 2021.

The demand for information security in higher education will continue to grow. A lack of risk management has already resulted in catastrophic data breaches, and these incidents are expected to continue. Academics, students, and universities around the world have been subjected to a constant torrent of attacks. This study relies on a review of several educational surveys. As a result of this research, new lines of study will be opened up for higher education security. Various sources have been analysed in this report to provide an overview of cyber security challenges. We categorise our review mostly based on the research questions that we address. In order to gain a foothold in the educational community, it will be beneficial to both corporations and academics.

Threats, Virtual learning Environment, Issues, E-Learning

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[7] J. Chapman "How Safe Is Your Data? Cyber-Security in Higher Education". HEPI Policy Note, April 2019.
[8] M. Whitman, H. Mattord, "Threats to Information Protection-Industry and Academic Perspectives: An annotated bibliography". J.Cybersecur. Educ. Res. Pract. 2016.
[9] K.Beaudin," The Legal Implications of Storing Student Data: Preparing for and Responding to Data Breaches".New Dir. InstitutionalRes. 37–48, 2017.
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[12] I. Ajie, "A Review of Trends and Issues of Cybersecurity in Academic Libraries". Libr. Philos. Pract1–20. Available online:https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=5803&context=libphilprac (accessed on 28 January 2021).
[13] A.Diaz, A.T Sherman,A. Joshi, "Phishing in an Academic Community: A Study of User Susceptibility and Behavior". arXiv: 1811.06078, 2018.
[14] T. Cuchta, B. Blackwood, T.R Devine, R.J Niichel , K.M. Daniels, C.H.Lutjens, S. Maibach, R. J Stephenson. "Human Risk Factors in Cybersecurity". In Proceedings of the 20th Annual SIG Conference on Information Technology Education, Tacoma, WA,USA, 3–5 October; pp. 87–92, 2019.
[15] M. Dadkhah, G. Borchardt, T. Maliszewski, ,"Fraud in Academic Publishing: Researchers Under Cyber-Attacks". Am. J. Med. 130, 27–30, 2017.
[16] D. Teixeira ,J.Silva, A.Alkhatib, Tsigaris, P. "Spam emails in academia: Issues and costs". Scientometrics 122, 1171–1181, 2020.
[17] G. Wangen, N. Hellesen, H.Torres, E. Brækken, "An empirical study of root-cause analysis in information security management ".In Proceedings of the SECURWARE-The Eleventh International Conference on Emerging Security Information, Systems and Technologies. International Academy, Research and Industry Association (IARIA), Rome, Italy, 10–14 September 2017.
[18] H. Kashiwazaki, "Personal Information Leak in a University, and Its Cleanup". In Proceedings of the 2018 ACM SIGUCCS Annual Conference; Association for Computing Machinery: New York, NY, USA,; pp. 43–50, 2018.
[19] A.Deepa E. C. Blessie,”Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.23- 27, 2018.
[20]A Survey on Impact of IoT Enabled E – Learning Services, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.178-183, 2018.

Ankita sharma, "Review on Major Cyber security Issues in Educational Sector," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.26-29, 2021.

Automatic shape recognition and feature extraction from images has become very significant in today’s digital word as the use of digital images has grown exponentially over the last few decades. Image processing is a method which uses computer algorithms to extract various useful information from the digital images. Image processing mainly involves visualization, pattern recognition, feature extraction, classification etc. Moment invariants have been used as features for image processing. Moments can provide features of an object that uniquely represent its shape. These features are independent of translation, scale and rotation. The aim of this paper was to investigate the usefulness of moment invariants for the feature extraction from digital images. Two experiments were conducted to test the moment invariant for rotation invariance and scale invariance. The study found that most of the seven features had minor fluctuations when rotating or scaling the image.

Moment Invariant, Image processing, Pattern recognition, Feature extraction

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[4] Jan Flusser, “Moment Invariants in Image Analysis”, Proceedings of World Academy of Science, Engineering and Technology, Vol 11, pp. 196-201, 2006.
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[11] Jan Flusser, Barbara Zitova, Tomas Suk, “Moments and Moment Invariants in Pattern Recognition”, Wiley Publications, 2009.
[12] M. Fachrurrozi, Saparudin, A. Lestari, O. Arsalan, Samsuryadi and Ermatita, “Multiple Face Image Feature Extraction Using Geometric Moment Invariants Method”, International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 219-224, 2019.
[13] B. P. H. K. M. D. Senarathna and R. M. T. P. Rajakaruna, “Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM”, 10th International Conference on Information and Automation for Sustainability (ICIAfS), pp. 197-202, 2021.
[14] S. Sharma and P. Khanna, “Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM”, Journal of digital imaging vol. 28, 1, pp. 77-90, 2015.
[15] S. Urooj and S. P. Singh, “Geometric invariant feature extraction of medical images using Hu`s invariants”, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1560-1562, 2016.
[16] Y. M. Vaidya, B. S. V. Swati and N. Kantipudi, “Moment Invariants based feature techniques for segmentation of retinal images using supervised method”, International Conference on Industrial Instrumentation and Control (ICIC), pp. 1373-1377, 2015.
[17] Y. Jusman, M. K. Anam, S. Puspita and E. Saleh, “Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features”, International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 296-299, 2021.
[18] Laura Keyes and Adam Winstanley, “Applying Computer Vision Techniques To Topographic Objects”, International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3, Amsterdam, pp. 480-487, 2000.
[19] Dehghan, M. and Faez, K., “Farsi Handwritten Character Recognition with Moment Invariants”, International Conference on Digital Signal Processing, Vol. 2, pp. 507-510, 1997.
[20] V. V. Agarkar, “Analysis of Sperm Morphology in Microscopic Images based on Moment Invariants”, M. Phil. Dissertation, Alagappa University, Karaikudi, 2009.

V.V. Agarkar, "Feature Extraction of Medical Images Using Moment Invariants," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.30-33, 2021.

Online research opinion mining and sentiment analysis of college and school going students may accurately represent the students learning circumstances, providing the theoretical foundation for further revisions of teaching programmes. Analysis of student learning experiences using data mining and sentiment analysis in online learning community may lay the theoretical groundwork for future changes to teaching programmes. The term "online study" is the study that takes place using the internet. One of the objectives of the project is the creation and assessment of a conceptual model that incorporates students` learning and teaching preferences as well as technological experience, as well as their feelings about how these things impact their learning and teaching. An online survey of college and school going students was performed. It was found that some clusters of students were formed after applying k-means clustering machine learning algorithm which shows us that some changes should be adopted in the current online study scenario. Prediction and visualization of the data is done by seaborn, matplotlib python libraries which helps us to understand the pattern of the data. It is expected that this assessment would create a better system for students to study. Discoveries corroborate hypotheses about the influence of sentiment on factors such as attitude, favorite hobbies, and technological experience.

online study, sentiment analysis, python, machine learning, clustering, k-means sert

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Mamta Tiwari, Swagata Dutta, "Sentimental Analysis of online study of College and School going Students," International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.34-42, 2021.