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Pipeline Corrosion Management System is the process of analyzing corrosion status in a crude pipeline by extracting data from a Linear Polarization Resistance (Probe) to monitor corrosion behavior. Petroleum pipelines are prone to corrosion due to the sea water injection into the wells to increased pressure required to force crude from the reservoir. Petroleum pipelines maintenance therefore becomes very necessary so that imminent growth of bacteria and corrosion are eliminated. Many researchers has done tremendous work in detection and treatment corrosion caused mainly by microbial growth but these systems does not recommend appropriate chemicals and quantities for treatment of different types of corrosion discovered. Hence to solve these problems stated we develop a Chemical-Based Pipeline Maintenance Decision Support System. This system offers an online management technique which reads the Probe, extracts corrosion details and proffer chemical treatment if corrosion is found. An Object Oriented Analysis and Design (OOAD) methodology was adopted, while we used PHP programming language at the front end, and MySQL relational database at the backend to achieve this goal. Our findings reveal that corrosion inhibitors and biocides are good treatments for corrosion and bacteria growths in oil pipelines. The Linear Probe is faster for pipeline corrosion detection, treatment and recommendation when compared to the existing Coupon method. This system will give confidence to pipeline operator when handling corrosion detection and treatment and make them deliver their work timely and less expensive

Pipeline, Decision Support System, Microbes, Corrosion, Biocides, Inhibitors

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W. Anyoku, E.O. Nwachukwu, B.O. Eke, "A Chemical-Based Pipeline Maintenance Decision Support System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.1-6, 2020.

Any Biometric system comprises five modules which are data acquisition, Pre-processing, feature extraction, matching and decision. Finger vein is another biometric innovation that contends with other ground-breaking biometrics modalities, for example, the face, palm print, fingerprint, iris and voice. Finger vein recognition is a biometric method used to analyze finger vein patterns of people for appropriate verification. The feature extraction module is very important in a biometric system. The extracted features perhaps include irrelevant and redundant features that can drive to the retreat of the performance of the biometric system. To solve this problem, an efficient feature selection scheme based on the Genetic Algorithm (GA) for Finger vein recognition is proposed. While feature extraction the work was divided into four scenarios based on the full feature, Principal Components Analysis (PCA) method for feature reduction, a hybrid of GA and PCA for feature reduction and selection, and GA for feature selection. The proposed method tested on two standard finger vein biometrics databases (SDUMLA-HMT and UTFV). The experimental results show that the proposed method gives the best results with high accuracy reached to 99.95% and 99.89595%

Finger-Vein, Biometrics, Genetic Algorithm, Feature Extraction, Gabor Filter, PCA, Correlation Coefficients, FAR, FRR

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Waleed Dahea, H.S. Fadewar, "An Efficient Feature Selection scheme based on Genetic Algorithm for Finger Vein Recognition," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.7-15, 2020.

Vehicular Ad hoc Networks (VANET) is an emerging technology and is type of an ad hoc mobile network which offered facilities like security, traffic proficiency, driving ease, traffic management services etc. with the help of vehicles communications. Traffic information is especially useful for the driver during driving, such as an accident alert or else traffic jam alert. In ad hoc vehicle networks, data transmission is usually performed by multi hop communication in which high-speed vehicles act as a data carrier. Vehicles are forced to navigate a defined route, depending on the route layout and traffic conditions. In an ad hoc vehicle network, delivering data to multiple stores is a very complicated job because of the high mobility and frequent disconnections that occur in vehicle networks. The biggest challenge in ad hoc vehicle networks is to gather information such as accidents, speed limits, road obstacles, road conditions, traffic conditions, commercial advertising, etc. for safety and convenience. In many diffusion techniques, the vehicle transports the package until it finds another vehicle heading towards destination and at that point passes the package to that vehicle. In this article we surveyed some research paper based on predictive data dissemination and try to put forward their research work. The surveyed research papers are based on different techniques and tools for data dissemination protocols.

Multi-beam adaptive array (MBAA), Distributed low-redundancy information sharing algorithm (DLRA), Road Side Units (RSUs), Receiver-oriented multiple access (ROMA)

[1] Ali, G. G. M. N., Rahman, M. A., Chong, P. H. J., & Samantha, S. K. “On Efficient Data Dissemination Using Network Coding in Multi-RSU Vehicular Ad Hoc Networks” . IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016.
[2] Felipe Domingos da Cunha, Leandro Villas, Azzedine Boukerche, Guilherme Maia, Aline CarneiroViana. “Data Communication in VANETs: Survey, Applications and Challenges”, Ad Hoc Networks, Elsevier, 44 (C), pp. 90-103, 2016.
[3] Zhang, Y., Peng, L., Xu, R., & Li, J. “A Distributed Low-Redundancy Information Sharing Algorithm in Ad Hoc Networks with Directional Antennas”, Procedia Computer Science, 131, pp. 1142–1149, 2018,
[4] Liu, L., Chen, C., Qiu, T., Zhang, M., Li, S., & Zhou, B. “A data dissemination scheme based on clustering and probabilistic broadcasting in VANETs”, Vehicular Communications, Volume 13, pp. 78–88, 2018.
[5] WU, C., OHZAHATA, S., & KATO, T. “VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism”, IEICE Transactions on Communications, E95-B(2), pp. 415–425, , 2012.
[6] Akhtar, N., Ergen, S. C., & Ozkasap, O. “Vehicle Mobility and Communication Channel Models for Realistic and Efficient Highway VANET Simulation”, IEEE Transactions on Vehicular Technology, 64(1), 248–262, 2015.
[7] Nikolovski, T., & Pazzi, R. W. “Delay Tolerant and Predictive Data Dissemination Protocol (DTP-DDP) for urban and highway vehicular ad hoc networks (VANETs)”, Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications - DIVANet ’16, pp 67–74, 2016.
[8] Dubey, Brij & Naveen, Chauhan & Kumar, Prashant. “A Survey on Data Dissemination Techniques used in VANETs”, International Journal of Computer Applications, Vol 10, No-7, pp 5-10, 2010.
[9] Liu, L., & Chen, D. “A Data Dissemination Method Based on Region Type Correlations for Mobile Opportunistic Networks”, Wireless Communications and Mobile Computing, pp.1–10 , 2018.
[10] Rakesh Shrestha and Seung Yeob Nam. “Trustworthy Event Information Dissemination in Vehicular Ad Hoc Networks”, Hindawi Mobile Information Systems Volume 2017, Article ID 9050787, pp.1-17, 2017.
[11] Ansari, S., Sánchez, M., Boutaleb, T., Sinanovic, S., Gamio, C., & Krikidis, I. “SAI: Safety Application Identifier Algorithm at MAC Layer for Vehicular Safety Message Dissemination Over LTE VANET Networks”, Wireless Communications and Mobile Computing, pp.1–17, 2018.
[12] Crawford, P. S., Al-Zarrad, M. A., Graettinger, A. J., Hainen, A. M., Back, E., & Powell, L. “Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning”, Advances in Civil Engineering, pp. 1–13, 2018.
[13] Ghaleb, F. A., Aizaini Maarof, M., Zainal, A., Rassam, M., Saeed, F., & Alsaedi, M. “Context-Aware Data-Centric Misbehaviour Detection Scheme for Vehicular Ad Hoc Networks using Sequential Analysis of the Temporal and Spatial Correlation of the Consistency between the Cooperative Awareness Messages”, Vehicular Communications, 100186, pp 1-17, 2019.
[14] Prabhjot Singh and Rasmeet Singh Bali. “Secure Data Dissemination Scheme for Vehicular Relay Network based on Predictive Clustering”, Indian Journal of Science and Technology, Vol9(27), 2016.
[15] Cooper, C., Franklin, D., Ros, M., Safaei, F., & Abolhasan, M. “A Comparative Survey of VANET Clustering Techniques”, IEEE Communications Surveys & Tutorials, 19(1), 657–681, 2017.

Deepak Gupta, Rakesh Rathi, Shikha Gupta, Neetu Sharma, "VANET Data Dissemination an Emerging Technology: A Survey," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.16-22, 2020.

The study of plant phenotype using segmentation techniques is one the leading research area in the field of agricultural technology. Plant phenotype is a technical term which is used to describe the observable characteristics of the plant like width, height, biomass, plant, leaf shape and so on. It is required in order to study about the physical characteristics of the plant like finding the area, height, width, structure of the plant and skeleton generation of the plant root etc. It is used in the field of agricultural technology to carry out various types of research. This paper explores the use of different segmentation methods in order to get efficient segmented images for the plant`s shoot and root systems. The segmentation methods used are threshold segmentation, edge detection, and followed by contour segmentation on PlantCV platform. The proposed work partitions the segmentation process in four steps, where the output of each step is given as input to the next step. We use the thresholding method as a first step in plant image segmentation process to remove the background and noise in the image. This step is followed by edge detection method to remove the unwanted regions and to detect false edges in a segmented plant image. Next, the contour segmentation is used to identify the complete structure of the plant. Then from the output image obtained, features are extracted in JSON format and the segmented images acquired are stored in an output folder.

Segmentation, Phenotype, Sobel operator, PlantCV platform, Shoot module, Root module, Contour Segmentation, Edge Detection, threshold Segmentation, Gaussian Blur, Median Blur, Region of Interest(ROI)

[1] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh,“ Fast and Accurate Detection and Classification of Plant Diseases“, International Journal of Computer Applications , Vol. 17,No.1, pp. 0975 – 8887, 2011.
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[9] N.Valliammal and Dr.S.N.Geethalakshmi, “Plant Leaf Segmentation Using Non Linear K means Clustering“, IJCSI International Journal of Computer Science, Vol 9, Issues9, Issue ISSN (Online): 1694-0814, 2016.
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Althaf S., Suresha N., Pooja K.S., Jeelani H. Muddebihal, Poonam Ghuli, Ramakanth Kumar P., "Study of Plant Phenotype using Image Segmentation Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.23-30, 2020.

Among many psychiatric disorders, Autism Spectrum Disorder (ASD) affects people in diverse ways. Nowadays the prevalence of Autism spectrum disorder has increased gradually worldwide. Difficulty in social interaction, trouble with speech and nonverbal communication, repetitive actions, avoidance of eye contact and abnormal facial expressions are the primary symptoms of ASD. Predicting ASD at an early stage is important to provide necessary developmental support. Machine Learning algorithms play a vital role in prediction of ASD. In this study, Machine Learning algorithms like Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision trees, Random forest –CART and merging of Random Forest-CART and Random forest - Iterative Dichotomiser 3 (ID3) are compared for ASD prediction.

Autism Spectrum Disorder, Autistic Spectrum Disorders in Children and Adults, Machine Learning, Linear Discriminant Analysis, K-Nearest Neighbour, SVM, K-NN, Decision Trees, Random Forest CART & ID3

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[4] Stevens, E., Atchison, A., Stevens, L., Hong, E., Granpeesheh, D., Dixon, D., & Linstead, E., “A Cluster Analysis of Challenging Behaviors in Autism Spectrum Disorder”. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). doi:10.1109/icmla.2017.00-85, 2017.
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[6] Omar, K. S., Mondal, P., Khan, N. S., Rizvi, M. R. K., & Islam, M. N., “ A Machine Learning Approach to Predict Autism Spectrum Disorder”. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). doi:10.1109/ecace.2019.8679454, 2019.
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[28] Rosy Mishra, Y. Sowjanya, Sushanta Meher, Mousumi Meher, "A Voice Signal Interpreter using Machine Learning Techniques," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.22-27, 2020.

Kalpana C., Anitha Kumari K., "Predicting Autism Spectrum Disorder Using Machine Learning Algorithms: A Review," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.31-36, 2020.

Today, the whole world is suffering from pandemic of Covid-19 diseases. Corona virus disease 2019 (COVID-19) is an illness caused by a novel coronavirus now called as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; formerly called 2019-nCoV), which was first identified amid an outbreak of respiratory illness cases in Wuhan City, Hubei Province, China. Today, every country in the world is trying to find the antidote of COVID-19. Meanwhile, a hope to find the medicine of Covid-19, can be seen through Quantum Computing. For the readers who are not familiar with quantum computation, a brief introduction to it is provided. Classical computers that are used today can encode information only in binary format that take the values of 1 or 0. This restricts the ability of classical computers. On the opposite hand, Quantum computing is a region of computing that deals with developing engineering supported the principles of scientific theory, that explains the behavior of energy and material at the atomic and subatomic levels. Quantum computing uses quantum bits or qubits. it`s the distinctive ability of subatomic participles that enables them to exist in additional than one states i.e. one and zero at an equivalent time. These supercomputers area unit supported by Superposition and entanglement, 2 vital options of physics. Quantum computing relies on the principles of scientific theory and uses the quantum-mechanical phenomena like superposition and entanglement to perform computations

Quantum computing, COVID-19, Quantum Computers, Superposition, Entanglement, Qubits

[1] Mr. Vishal Ramchandra Gotarane, Mr. Sushant Savita Madhukar Gandhi, “Quantum Computing: Future Computing”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056, Vol. 03, Issue. 02, pp.1024-1026, 2016
[2] Phillip Kaye , Raymond Laflamme , and Michele Mosca , “An Introduction to Quantum Computing” Oxford U. Press, New York, pp 274, 2007. ISBN 978-0-19-857000-4, ISBN 978-0-19-857049-3 paper
[3] Robert S. Sutor, “Dancing with Qubits:How quantum computing works and how it can change the world ”, Packt publishing Ltd., UK, pp.2-21, 2019
[4] Bertels, K., "Quantum computing: How far away is it?," in High Performance Computing & Simulation (HPCS), 2015 International Conference on , vol., no., pp.557-558, 20-24 July 2015
[5] Morimae, T., "Basics and applications of measurement-base quantum computing," in Information Theory and its Applications (ISITA), 2014 International Symposium on , vol., no., pp.327-330, 26-29 Oct. 2014
[6] Yousuf Mohammed Faroukh, “ Quantum Computers Vs Conventional Computers: A Study on the Larger Scale “,pp. 7-8, 24 march, 2018.
[7] Heni Bouhamed, “Covid-19 Cases and Recovery Previsions with Deep Learning Nested Sequence Prediction Models with Long Short-Term Memory (LSTM) Architecture”, International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.10-15, April (2020)

Deepa Sonal, "Quantum Computing:A possibility to find COVID-19 antidote," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.37-42, 2020.

Image enhancement is one of the basic steps used in digital image processing. Here the image is manipulated to make it more suitable than the original image for specific purposes. It is used to modify the contrast of an image. Here the intensity of the input image is manipulated to make the best use of available grayscale values. A wide range of contrast enhancement methods available work upon the histogram of an image to make the image visually suitable for either viewing or further development. We need to study and review different contrast enhancement techniques primarily operating on the histogram of an image. Depending on the nature of the technique these are classified into global and local contrast enhancement techniques. This paper focuses on a comparative study of contrast enhancement techniques and draws conclusions considering their pros and cons

Contrast enhancement, histogram equalization

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Satyajit Ray Pradhan, Chandra Sekhar Panda, "Review of Contrast Enhancement Techniques Based on Histogram Processing," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.43-47, 2020.

The primary aim of this paper is to present simulation findings and design network signal booster at a frequency of 900 MHz RF. The booster is designed to work on GSM system bandwidth. The gain given by the amplifier circuit was 12dB at the middle frequency with a difference of +-1.3 dB to normalize. The monopole antennas used for signal reception and transmission that have 0.9141-1315i input impedance and -18.18dB return loss. The study includes detailed graphical and tabular view of the results at each design level, simulation

Amplifier, noise figure, Low noise Stability, Impedance matching

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[3] Domine Leenaerts, Jos Bergervoet, Jan-Willem Lobeek,MarekSchmidtSzalowski“900MHz/1800MHz GSM BaseStation LNA with Sub-1dB Noise Figure and +36dBm OIP3”NXP Semiconductors, Eindhoven, 5656AE, the Netherlands
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pp.2699-2707.
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[9] Youming Zhang, Xusheng Tang and Dawei Zhao, “A 0.7–9GHz CMOS broadband high amplifier for multi-band use”, IEEE inter conference on Microwave and Millimeter technology, May 2012.
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[11] S. Jing,, Y. Yin, A. Sun, , Y. Wei,, & Y. Yang,.” Compact E-shaped monopole antenna for dual-band WLAN applications”. IEEE International Conference (ICMTCE), pp. 305-308, May. 2011
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[13]Aleksandar Tasić."Performance Parameters of RF Circuits", Analog Circuits and Signal Processing Series, 2006

Saksham Jain, Vikrant Saini, Brajlata Chauhan, "Simulation and Designing of Network Signal Booster at 900 MHz," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.48-52, 2020.

The advent of social media has changed the ways of human communication. It has brought people around the world closer to each other. Despite its innumerable benefits, social media is considered to be one of the harmful elements of society. Cyberbullying and online harassment are the most common negative effects of social media. Cyberbullying is a way of bullying someone with the use of technology and it can take place through many forms such as SMS, Apps, online gaming, social networking sites online forums, etc. The project aims at detecting cyberbullying content based on textual features. The system detects various language patterns often used by bullies. This is accomplished using machine learning. The proposed system uses voting classifier to classify the input text as ‘Bullying’ or ‘Non-Bullying’. It also compares the accuracies of various classifiers and introduces a framework of supervised machine learning to detect cyberbullying in textual data. It is observed that a voting classifier i.e. a combination of the Logistic Regression, Random Forest, Support Vector Machine, SGD classifier gives the highest accuracy and precision i.e. 74% and 77% respectively. This trained model is deployed on a webpage which makes the system user intuitive and user-friendly

Cyberbullying, Machine Learning, Classification, Voting classifier, Social Media

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[3] R. Zhao, and K. Mao, "Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder.", IEEE Transactions on Affective Computing , Vol.8, Issue.3, pp. 328-339, 2016.
[4] M. A. Al-garadi, K. D. Varathan, and S. D. Ravana, "Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network.", Computers in Human Behavior, Vol.63, pp. 433-443, 2016.
[5] A. Mangaonkar, A. Hayrapetian, and R. Raje, "Collaborative detection of cyberbullying behavior in Twitter data.”, 2015 IEEE International Conference on Electro/Information Technology (EIT), pp. 611-616, 2015.
[6] V. Nahar, X. Li, H. L. Zhang, and C. Pang, "Detecting cyberbullying in social networks using multi-agent system." Web Intelligence and Agent Systems: An International Journal, Vol.12, Issue.4, pp. 375-388, 2014.
[7] K. Reynolds, A. Kontostathis, and L. Edwards, "Using machine learning to detect cyberbullying.”, In 2011 10th International Conference on Machine learning and applications and workshops, IEEE, Vol.2, pp. 241-244, 2011.
[8] K. Dinakar, R. Reichart, and H. Lieberman, "Modeling the detection of textual cyberbullying.", In fifth international AAAI conference on weblogs and social media, 2011.

R. Kaur, M.S. Sagar, "Detection of Cyberbullying using Voting Classifier," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.53-60, 2020.

Breast cancer is an uncontrolled growth of breast cells and the most common invasive cancer in women, the second leading cause of cancer death in women next to lung cancer. Cancer starts from breast and spreads to other parts of the body. People are unable to identify the disease before it becomes dangerous. It can be cured if the disease is identified at an earlier stage. Awareness of breast cancer, public attentiveness, and advancement in breast imaging has made a positive impact on the identification and screening of breast cancer. The interpretation of a tumor image is taken from patients and stored in datasets. This study suggests a feature extraction method such as PCA (Principal Component Analysis) which is used for pre-processing the data and extracting the most relevant features. Several classifiers like K-Nearest Neighbour (KNN), Naïve Bayes(NB), Linear Support Vector Machine(L-SVM), Gaussian Kernel Support Vector Machine(K-SVM), Logistic Regression(LR) are used to build machine learning model, among these classifiers Linear kernel Support Vector Machine (L-SVM) gives better accuracy. The proposed system uses a Linear kernel Support vector machine(L-SVM) to perform staging. The objective of the project is to carry out dimensionality reduction on cancer datasets and to build a predictive model for multi-class cancer stage classification using a linear kernel SVM classifier

Classification Techniques, Feature extraction, Principal Component Analysis(PCA) k-Nearest Neighbor (KNN), Linear Support Vector Machine (L-SVM), Gaussian Kernel Support Vector Machine(K-SVM) , Naïve Bayes (NB), Decision Tree (DT), Logistic Regression (LR)

[1] MadhuKumari and Vijendra Singh, “Breast Cancer Prediction system “. In the proceedings of the 2018 International Conference on Computational Intelligence and Data Science (IJCSES), India, Vol.132, p.371-376, 2018.
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[4] Ajay Kumar, R. Sushil, A. K. Tiwari, “Comparative Study of Classification Techniques for Breast Cancer Diagnosis,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.234-240, 2019.
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[9] Ajay Kumar, R. Sushil , A. K. Tiwari.” Comparative Study of Classification Techniques for Breast Cancer Diagnosis”. International Journal of Computer Science and Engineering(IJCSE), Vol.-7, Issue-1, p.234-240 Jan 2019.
[10] Vikas S, Thimmaraju S N. “Breast Cancer Diagnosis and Classification Using Support vector machines With Diverse Datasets”. International Journal of Computer Science and Engineering(IJCSE), Vol.-7, Issue-4, p.442-446,April 2019.

Jency Gracy Bai A., Lathikaa Sri M., Jayalakshmi M., Harinii M., K. Amshakala, "Multi-Class Cancer Classification Using Dimensionally-Reduced Breast Cancer Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.61-69, 2020.