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Water is a precious commodity and should not be wasted. It has always been a challenging issue worldwide to prevent wastage of potable water. Inspecting the entire water pipeline system of a house each time to find a leak or issue can be time consuming and costly if water is being wasted for a long period of time. People like to complete their daily household chores without any delay and find it frustrating when their work is halted because of unavailability of water arising from damages in their home water piping system. It is always a headache when there is leakage in the pipe systems at home. People may be away from their home, at work or on vacations. Pipe bursting goes unnoticed and resulted in consequent damages. Smart system can surely be helpful in preventing those unwanted surprises. This paper presents an implementation of a smart home water protection system that proves to be beneficial in saving up water, money and time. The smart system notifies the user about damages in home water pipeline systems while simultaneously taking measures to prevent further losses. Warning messages are issued to people thus keeping them informed about the problematic state of their water pipeline system. Alternatively, the smart system can take actions automatically to temporarily remediate the problem or allow the house owner to do otherwise. The system provides the user with a simple yet intuitive interface where information about the condition of the water pipeline system is in a conclusive way.

leakage, pipeline, pump, smart technology, water-flow

[1] S. Pirbhulal, H. Zhang, E. Alahi , M. Ghayvat, C. Zhang, W. Wu, “A novel secure IoT-based smart home automation system using a wireless sensor network”, Sensors, Vol.17, Issue.1, pp.69, 2017.
[2] V. Radhakrishnan, W. Wu “IoT technology for Smart water system”, In the Proceedings of the 2018 IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp.1491-1496, 2018.
[3] S. Raut, S. Motade, “IOT Based Smart Irrigation System using Cisco Packet Tracer”, International Journal of Computer Sciences and Engineering, Vol. 9, Issue 2, pp. 12-16, 2021.
[4] Y. Badamasi, “The working principle of an Arduino”, In the Proceedings of the 2014 IEEE 11th international conference on electronics, computer and computation (ICECCO), pp. 1-4, 2014.
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[6] L. Boaz, S. Kaijage, R. Sinde, “Wireless sensor node for gas pipeline leak detection and location”, International Journal of Computer Applications, Vol. 100, Issue 18, pp. 29-33, 2014.
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[9] M. Daadoo, Y. Daraghmi, “Smart Water Leakage Detection Using Wireless Sensor Networks (SWLD)”, International Journal of Networks and Communications, Vol. 7, Issue 1, pp. 1-16, 2017.
[10] S. Thenmozhi, K. Sumathi, B. Asokan, R. Priyanka, R. Maheswar, P. Jayarajan, "IoT Based Smart Water Leak Detection System for a Sustainable Future", In the Proceedings of the 2021 IEEE 6th International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 359-362, 2021.
[11] M. Maureira, D. Oldenhof, L. Teernstra, “ThingSpeak–an API and Web Service for the Internet of Things”, World Wide |Web, pp. 1-8, 2011.
[12] S. Pasha, “ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis”, International Journal of New Technology and Research (IJNTR), Vol. 2, Issue 6, pp. 19-23. 2016

A. Q Mohabuth, T. Pal, M. Sobrun, "Development of an IoT-Based Smart Home Water Leak Detection System," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.1-5, 2021.

It is well known that the physical properties of ferroelectric thin films are considerably different from those of bulk ferroelectrics. The surface effect is one of this important properties observed in ferroelectric thin films. At the surface , the coordination of the atoms is different from that in the volume of the film. The influence of the surface effect on the ferroelectric properties of PbTiO3 films has been investigated based on Ginsburg- Landau-Devonshire (GLD) thermodynamic theory, where the free energy coefficients are calculated for PbTiO3 crystal from microscopic interactions based upon the statistical mean field approximation. In this study, the shift displacement profile, the average the maximal and minimal of central ion shift displacement, the phase transition and Curie temperature (Tc) dependence on the film thickness are numerically modeled and analyzed by an Euler-Lagrange equation with applying a corresponding boundary condition on ion displacement for various values film thicknesses and levels of extrapolation length.

Surface effect, Size effect, Phase transition, Curie temperature, Ginzburg-Landau-Devonshire theory

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[19] K Nakamura,. and W. Kinase. “Theory of the successive phase transition in BaTi03”, Journal of the Physical Society of Japan. Vol. 61, pp, 4596-4614, 1992
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E. ES-Salhi, A. Toumanari, "Devonshire-Ginzburg-Landau phenomenological study of ferroelectric properties of PbTiO3 thin films.," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.6-9, 2021.

The aim of this research is to investigate performance metrics such as bit-error-rate (BER), throughput, spectral efficiency, peak-to-average power ratio (PAPR) and one-way latency for the different modulation scheme used in a 5G wireless network. The Quadrature Phase Shift Keying (QPSK), 16QAM (16-Quadrature Amplitude Modulation), 64QAM and 256QAM were studied for signal to noise ratio (SNR) ranging from -10dB to 20dB at intervals of 5dB. It was experimentally demonstrated through MATLAB simulation that the efficiency of the 5G network in various aspects depends on the modulation coding scheme (MCS) used. The QPSK MCS exhibited the best performance in BER with 0 bps at all SNRs, throughput of 100%, PAPR of 3dB and one-latency of 1.75ms. However, it suffered a major drawback in spectral efficiency because of the small modulation order. The 256QAM might be the choicest of all considering its number of bits per symbol mapping when the SNR is around 10 to 15dB. However, it performs very poorly when the signal to noise ratio is low, largely due to the reduced hamming distance between the symbols, making it prone to Inter Symbol Interference (ISI). At the worst SNR of -10dB, 256QAM has a BER of 0.5008bps, 0% throughput, maximum PAPR of 7dB and latency of 5.25ms with a block error probability of 1. The 64QAM modulation scheme exhibits a good compromise at a reasonable SNR of 5dB. It has a BER of 0.0, achieves a throughput of 100% and a spectral efficiency of 0.0295 bps/Hz.

Modulation, fifth generation, performance, bit-error-rate, throughput, spectral efficiency, peak-to-average power ratio, one-way latency

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[3] Z. Pi and F. Khan, “An introduction to millimeter-wave mobile broadband systems,” IEEE Communication, vol. 49, no. 6, pp. 101-107, 2011.
[4] P. Banelli, S. Buzzi, G. Colavolpe, A. Modenini, F. Rusek and A. Ugolini, “Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM?,” IEEE SIGNAL PROCESSING MAGAZINE, vol. 1, pp. 11-13, 2014.
[5] L. Atzori, A. Iera and G. Morabito, “The Internt of Things: A survey,” Elsevier Computer Network, vol. 54, no. 15, pp. 2787-2805, 2010.
[6] G. Wu, S. Talwar, K. Johnsson, N. Himayat and K. D. Johnson, “M2M: From mobile to embedded Internet,” IEEE Communication Magazine, vol. 49, no. 4, pp. 36-43, 2011.
[7] G. Fettweis, “The tactile Internet: Appllications and challenges,” IEEE Vehicle Technology Magazine, vol. 9, no. 1, pp. 64-70, 2014.
[8] S. Ghosh, “Performance Evaluation of Different Coding and Modulation Scheme in LTE Using Different Bandwidth and Correlation Levels.,” Wireless Pers Commun, vol. 86, p. 563–578, 2016.
[9] S. Tholhath and T. C. Tiong, “Performance Analysis for LTE Wireless Communication,” IOP Conf. Series: Materials Science and Engineering, vol. 78, pp. 1-9, 2015.
[10]E. Ayanoglu, “5G Today: Modulation Technique Alternatives,” in International Conference on Computing, Networking and Communications (ICNC), Invited Symposium, Kauai, Hawaii, USA, 2016.
[11]D. D. Sharma and K. D. Singh, “PERFORMANCE ANALYSIS OF MODERN MODULATION TECHNIQUES FOR 5G NETWORKS,” International Journal of Research and Analytical Reviews (IJRAR), vol. 5, no. 4, pp. 3-5, 2018.
[12]T. Rajput and M. L. Jatav, “Review Paper on Massive MIMO Systems with Channel State Information,” International Journal of Computer Science and Engineering, vol. 7, no. 6, pp. 1-6, 2019.
[13]A. Gupta and A. Khare, “Average Spectrum Efficiency of Non-Orthogonal Multiple Access (NOMA) for 5G,” Internataional Journal of Computer Science and Engineering, vol. 5, no. 7, pp. 1-2, 2019.
[14]H. Kim, “Design and Optimization for 5G Wireless Communications,” Hoboken, USA, John Wiley & Sons Ltd, pp. 199-200, 2020.

K.O. Ogbeide, O.J. Daramola, "Efficiency Comparison of Different Modulation Scheme for 5G Application Using Simulation Approach," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.10-18, 2021.

Lupus, often detected as a chronic disease, is beyond any measure of cure. ANN (Artificial Neural Network) is used to suspect lupus disease in this research paper. If treated in an early stage, this disease can be controlled. Early diagnosis of lupus is required to treat it properly. It is very difficult to diagnose lupus manually by observing various symptoms. An approach is given to diagnose lupus in an efficient way with the help of ANN. An ANN has been designed here to suspect lupus based on laboratory test reports. Lupus is a chronic disease. The ANN consists of many neurons associated with weights. Each test report is dependent on the existence of each neuron. The present paper aimed at designing an Artificial Neural Network model to diagnose the stage of Lupus. Here the data has been collected from North Bengal Medical College for training the network. The proposed ANN used here is a supervised type, where different patterns represent different status of patient.

SLE, Hematocrit, WBC, SLT

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P. Saha, R.K. Mandal, "Lupus Suspection Expert System Using Artificial Neural Networks (ANN)," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.19-23, 2021.

Publishers are getting content frequently as demand of publication increases day by day. To resolve an issue of identifying the research paper class as per content this work proposed a hybrid model. Features were select by the fish schooling genetic algorithm and indexing was provide by hash structure. In order to maintain the privacy of the user and server data model work on key based searching of relevant document. Each document has set of keywords and each keyword has its own unique key. So user query pass as set of unique keys and searching of cluster document was done by matching keys with hash index. Experiment was done on real dataset having set of document from different field of publication. Result shows that proposed model FSGA has increases the result outcome by fetching more relevant text documents as per user query.

Clustering, Genetic Algorithm, Text Mining, Pattern Feature

[1]. Abhishek Jain, Aman Jain, Nihal Chauhan, Vikrant Singh and Narina Thakur. “Information Retrieval using Cosine and Jaccard Similarity Measures in Vector Space Model”. International Journal of Computer Applications 164(6):28-30, April 2017.
[2]. Dr.M.Suresh Babu, Mr. A.Althaf Ali, Mr. A.Subramaneswara Rao, “A Study on Information Retrieval Methods in Text Mining”, International Journal Of Engineering Research & Technology (Ijert) Ncdma, Volume 2 – Issue 15, 2014
[3]. P, Mrs. (2020). A Prognostic Rainfall using Machine Learning Technique. International Journal for Research in Applied Science and Engineering Technology. 8: 1 2020.
[4]. Wu Chuhan, et al. A hybrid unsupervised method for aspect term and opinion target extraction Knowledge-Based Systems, 148, 2018.
[5]. Giannakopoulos, Athanasios "Unsupervised aspect term extraction with b-lstm & crf using automatically labelled datasets." Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2017.
[6]. Zhitao Guan, Xueyan Liu, Longfei Wu, Jun Wu, Ruzhi Xu, Jinhu Zhang, Yuanzhang Li, Cross-lingual multi-keyword rank search with semantic extension over encrypted data, Information Sciences, Volume 514, 2020.
[7]. Jeong, Soyeong and Baek, Jinheon and Park, ChaeHun and Park, Jong. "Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation". Proceedings of the Second Workshop on Scholarly Document Processing, 2021.
[8]. H. Chiranjeevi and K. S. Manjula, "An Text Document Retrieval System for University Support Service on a High Performance Distributed Information System," 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2019.
[9]. Soyeong Jeong, Jinheon Baek, ChaeHun Park, Jong C. Park. "Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation". Proceedings of the Second Workshop on Scholarly Document Processing, pages 7–17 June 10, 2021.
[10]. Tuyen Thi-Thanh Do; Dang Tuan Nguyen. "A computational semantic information retrieval model for Vietnamese texts" International Journal of Computational Science and Engineering Vol.24 No.3., 2021
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[12]. Alan Díaz-Manríquez , Ana Bertha Ríos-Alvarado, José Hugo Barrón-Zambrano, Tania Yukary Guerrero-Melendez, And Juan Carlos Elizondo-Leal. “An Automatic Document Classifier System Based on Genetic Algorithm and Taxonomy”. accepted March 9, 2018, date of publication March 15, 2018, date of current version May 9, 2018.
[13]. Vinod Sharm, “Document Class Identification Using Fire-Fly Genetic Algorithm and Normalized Text Features” Volume 6 Issue 1, ijsret.com.
[14]. Vinod Sharma, Dr. Shiv Shakti Shrivastava and Dr. Sanjeev Kumar Gupta.“Fish Schooling Genetic Algorithm for Text document Clustering Using Pattern Features”. International Journal of Grid and Distributed Computing (I.J.G.D.C.) Vol. 13, No. 1, 2020.

Vinod Sharma, "Fish Schooling Algorithm and Hash Based Indexing for Text Document Retrieval," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.24-28, 2021.

Risk to human astronauts and interplanetary distance causing slow and limited communication drives scientists to pursue an autonomous approach to exploring distant planets, such as Mars. A portion of exploration of Mars has been conducted through the autonomous collection and analysis of Martian data by spacecraft such as the Mars rovers and the Mars Express Orbiter. The autonomy used on these Mars exploration spacecraft and on Earth to analyze data collected by these vehicles mainly consist of machine learning, a field of artificial intelligence where algorithms collect data and self-improve with the data. Additional applications of machine learning techniques for Mars exploration have potential to resolve communication limitations and human risks of interplanetary exploration. In addition, analyzing Mars data with machine learning has the potential to provide a greater understanding of Mars in numerous domains such as its climate, atmosphere, and potential future habitation. In order to explore further utilizations of machine learning techniques for Mars exploration, this paper will first summarize the general features and phenomena of Mars to provide a general overview of the planet, elaborate upon uncertainties of Mars that would be beneficial to explore and understand, summarize every current or previous usage of machine learning techniques in the exploration of Mars, explore implementations of machine learning that will be utilized in future Mars exploration missions, and explore machine learning techniques used in Earthly domains to provide solutions to the previously described uncertainties of Mars.

machine learning applications, Mars, autonomy

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Ali Momennasab, "Machine Learning for Mars Exploration," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.29-38, 2021.

Firewalls play important role in any organization today. Firewall is a layer of security between organization Network and the Internet. A Firewall is programmed with security rules that prevent unregulated access to your internal Network. The rules might control employee access to websites and also prevent files leaving the company over Network. Any number of rules can be used to protect the data. In this literature survey, we studied the work carried out by various researchers about the protection of Network and data security.

Firewall, Data Security, Network Protectio, ICT Infrastrcuture, Web Filtering

[1] Okumoku-Evroro, Oniovosa, “Application of Firewall system to Internet security”,International journal of information technology and business management,Vol.15 No.1,pp.64,2015.
[2] Okumoku-Evroro, Oniovosa, “Application of Firewall system to Internet security”,International journal of information technology and business management,Vol.15 No.1,pp.64,2015.
[3] Sezer YILDIZ , Umut ALTINI?IK, “ Detecting and preventin cyber attacks on local area Networks: A working example”, International journal of computer science and engineering, Vol.6,Issue11,2018.
[4] Seny Kamara, Sonia Fahmy, Eugene Schultz, Florian Kerschbaum, and Michael Frantzen, “Analysis of Vulnerabilities in Internet Firewalls”Vol.22,No.3,pp.1-19,2003.
[5] Lin, Ding Zhang, Yuqing Fu, Shuxian Wang, “A Design of the Ethernet Firewall Based on FPGA Shunhao”,International Congress on Image and Signal Processing,BioMedical Engineering and Informatics, Shanghai, China,pp.1-5, ISBN: 978-1-5386-1937-7, 2017.
[6] Nastassja Gaudet, Ana E Goulart, Edmond Rogers, Abhijeet Sahu, Kate Davis, “Firewall Configuration and Path Analysis for Smart Grid Networks”. International Workshop Technical Committee on Communications Quality and Reliability, Stevenson, WA, USA, pp.1-6, ISBN:978-1-7281-6627-8, 2020.
[7] Victor Clincy, Hossain Shahriar “Web Application Firewall: Network Security Models and Configuration”,42nd IEEE International Conference on Computer Software & Applications, Tokyo, Japan, pp.1-2, ISBN:978-1-5386-2667-2, 2018.
[8] Nitin Naik and Paul Jen-kins, “Enhancing Windows Firewall Security Using Fuzzy Reasoning” by 14th Intl Conf on Dependable, Autonomic and Secure Computing, Auckland, New Zealand pp.1-7, ISBN:978-1-5090-4065-0, 2016.
[9] Ricardo M. Oliveira Sihyung Lee Hyong S. Kim, “Automatic detection of Firewall misconfigurations using Firewall and Network routing policies”. pp.1-6, 2009.
[10] Qiumei Cheng, Chunming Wu, Haifeng Zhou, Yuhang Zhang, Rui Wang, Wei Ruan, “Guarding the Perimeter of Cloud-based Enterprise Networks: An Intelligent SDN Firewall”. 20th International Conference on High Performance Computing and Communications, Exeter,UK, ISBN:978-1-5386-6614-2, 2018.
[11] Perumalraja Rengaraj, S.Senthil Kumar and Chung-Horng Lung “Investigation of Security and QoS on SDN Firewall Using MAC Filtering”. International Conference on Computer Communication and Informatics, Coimbatore, INDIA, ISBN:978-1-4673-8855-9. 2017
[12] Benfano Soewito “Next Generation Firewall for Improving Security in Company and IOT Network”. International Seminar on Intelligent Technology and Its Applications, pp.1-5 ISBN:978-1-7281-3749-0, 2020.
[13] Robert Winding, Timothy Wright and Micheal Chappel. “ System Anomaly Detection: Mining Firewall Logs”,ISBN:1-4244-0422-3CD:1-4244-0423-1, 2006.
[14] Hitoshi Iyatomi, Michiaki Ito, “Web Application Firewall using Character-level Convolutional Neural Network”.14th International Colloquium on Signal Processing & its Applications, Penang, Malaysia.pp.1-4, 2018.
[15] Manoj Chakravati, “Next Generation Firewall”, International Journal of Computer Science and Information Technologies, Vol.7(3), pp.1-4, 2016.

Gayatri Deshmukh, Rachana Kamble, Pratap Singh Solanki, "Protection of Network Devices and Data security using Firewall: A literature Survey," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.39-44, 2021.

Fashion industry and innovation go hand in hand & technology could not be left far behind when it comes to innovation. Retail fashion is one of the early adopters of artificial intelligence when it comes to product development. AI based applications provides ease of search and shop for products. Either in the form of visual based search or suggesting products from same category with different attributes, retailers are providing every possible easement to customers for better shopping experience. With AI onboard, there is a huge infrastructure cost associated as well. In computer vision (AI), model training requires a good image data with labels & high-capacity platform for starters. Considering these facts, only using transformed feature vector of product images to generate clusters based on feature similarity can reduce the data dependency. Additionally, distance metric can be used to compute the feature distances & retrieval of top-k similar images by reverse indexing of image features to their corresponding images.

CNN (Convolution Neural Network), AI (Artificial Intelligence), Image Processing, Clustering, Feature Extraction, Unsupervised image-based recommender System

[1]. B. P. Amiruddin and R. E. Abdul Kadir, "CNN Architectures Performance Evaluation for Image Classification of Mosquito in Indonesia", 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2020, pp. 223-227, doi: 10.1109/ISITIA49792.2020.9163732.
[2]. Aayush Kumar Singh, Abhishek Kumar and Kuldeep. "Image to Image Search using K-means Clustering". International Journal of Computer Applications (0975 – 8887) Volume 182 – No. 46, pp. - March 2019
[3]. D. M. Chan, R. Rao, F. Huang and J. F. Canny, “T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data", 2018 30th International Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD), 2018, pp. 330-338, doi: 10.1109/CAHPC.2018.8645912.
[4]. Yash Baid, Avinash Dhole, "Food Image Classification Using Deep Learning Techniques", International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.11-15, 2021
[5]. Astha Pathak, Avinash Dhole, "Image classification Method in detecting Lungs Cancer using CT images: A Review", Interna-tional Journal of Computer Sciences and Engineering, Vol.9, Is-sue.5, pp.37-42, 2021.

Bagyalakshmi V., Gaurav Sharma, Meghna Mahajan, Muzzammil Ahmed, Kalyan Prakash Baishya, Kuruvilla Abraham, "Hybrid ML Recommender System for Visually Similar Product Images," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.45-50, 2021.

Software-Defined Networking is an emerging network architecture approach that enables us to control or, program the network system intelligently and centrally using software applications. This allows the user to manage the entire network consistently and holistically, regardless of the underlying network technology. By installing and configuring OpenDaylight which is a modular open platform for customizing and automating networks of any size and scale, we then configured it by Mininet in Virtual Machine. Finally, packets are captured by Wireshark and help us to measure the scenario. In this paper, our purpose is to shed light on SDN related issues and give insight into the challenges facing the future of this revolutionary network model, from both protocol and architecture perspectives. Additionally, we aim to present different existing solutions and mitigation techniques that address SDN scalability, elasticity, dependability, reliability, high availability, resiliency, security, and performance concerns.

Software Defined Networking, OpenDaylight, Mininet, Virtual Machine, Protocol

[1] S. Schaller, D. Hood, “Software defined networking architecture standardization” Computer Standards & Interfaces Vol.54, Issue.P4, pp.197–202, 2017.
[2] V. R. Tadinada, “Software Defined Networking: Redefining the Future of Internet in IoT and Cloud Era” in 2014 International Conference on Future Internet of Things and Cloud, Barcelona, Spain, 27-29 Aug, 2014.
[3] Celio Trois, M. D. Del Fabro, L. C. E. de Bona and M. Martinello, “A Survey on SDN Programming Languages: Toward a Taxonomy,” IEEE Communications Surveys & Tutorials, Vol.18, Issue.4, pp.2687–2712, 2016.
[4] Ankita V. Mandekar, K. Chandramouli, “Centralization of Network Using Openflow Protocol” Indian journal of science and technology Vol.8, Issue.S2, pp.165-170, Jan 2015.
[5] S. K. S. Yusof, P. E. Numan, K. M. Yusof, J. B. Din, M. N. B. Marsono, A. J Onumanyi, “Software-Defined Networking (SDN) and 5G Network: The Role of Controller Placement for Scalable Control Plane” in 2020 IEEE International RF and Microwave Conference (RFM), Kuala Lumpur, Malaysia, 14-16 Dec, 2020.

S.M. Bayazid Khan, M.M.H. Shojib, "Design and Implementation of Tree Topology in Software Defined Networking (SDN) using Mininet and OpenDaylight," International Journal of Computer Sciences and Engineering, Vol.9, Issue.11, pp.51-67, 2021.