The word sustainability is one of the World‟s most talked about but least understood word, this is because its meaning is often clouded by different interpretations. Sustainability embraces the preservation of the environment as well as critical development-related issues such as the efficient use of resources, continual social progress, stable economic growth, and the eradication of poverty. It is however, important to note that the roles of Quantity Surveyors in sustainability of built environment cannot be overemphasized. Ensuring sustainability in built environment involves a multidisciplinary and trans-disciplinary approach involving experts from different disciplines and stakeholders associated with construction sectors which include Quantity Surveyors in order to develop holistic and informed strategies that provide sustainable built environment...........

Keywords: Built environment, Cost Appraisal, Quantity Surveyors, Sustainability

[1]. Addis, W., Schouten, J. (2004) Design for Deconstruction, Principles of design to facilitate reuse and recycling (C607) London: CIRIA .
[2]. Ashworth, A. and Hogg, K. (2007) Willis Practice and Procedure for Quantity Surveyors 12th Ed, Blackwell Publishing, UK.
[3]. Asif, M., Muneer, T. and Kelly, R. (2007) Life cycle assessment: A case study of a dwelling home in Scotland, Building and Environment 42, 1391.
[4]. BREEAM, (2008) Sustainable Strategy for Construction, London, (The Building Research Establishment Environmental Assessment Method - BREEAM)
[5]. Edwards, B. and Hyett, P. (2001) Rough Guide to Sustainability, RI BA Publications, London.

This paper focuses on the consideration of various optimization criteria forsolving task scheduling problems in cloud environments. In past, several optimization techniques have been implemented. But most of these methods are time-consuming and takes a lot of search space to obtain a solution for task scheduling. Generally, two objectives have been considered with minimization of makespan and very few are dealt with less processing cost during task scheduling. However, it is noticed that the developed algorithm comprises a number of tuning parameters which leads the mathematical complexity and takes more search space while implementation. This paper demonstrates the possible number of objectives that can be treated to solve task scheduling problems.

Keywords: cloud computing, task scheduling, optimization, makespan, and processing cost.

[1]. Arunarani, A. R., Manjula, D., &Sugumaran, V. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407-415.

[2]. Bhoi, U., and Ramanuj, P. N. (2013). Enhanced max-min task scheduling algorithm in cloud computing. International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2(4), 259-264.

[3]. Chen, H., Wang, F., Helian, N., &Akanmu, G. (2013, February). User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In 2013 National Conference on Parallel computing technologies (PARCOMPTECH) (pp. 1-8). IEEE.

[4]. Ge, Y., & Wei, G. (2010, October). GA-based task scheduler for the cloud computing systems. In 2010 International Conference on Web Information Systems and Mining (Vol. 2, pp. 181-186). IEEE.
[5]. Goddu R. and Reddi K K (2019), Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments,International Journal of Recent Technology and Engineering, 8(2), pp. 2952-2958.

Credit card fraud is a type of identity theft that pose a serious problem to financial services. Every year, fraudulent credit card transactions cause billions of dollars of loss. Detecting credit card fraud is a major problem among various researchers. Many researchers have proposed various approaches to credit card fraud detection, few among which use machine learning algorithms. The current work is based on the Principal Component Analysis (PCA) technique along with the K-Means clustering algorithm for the analysis of credit card fraud data. PCA is a tool in data analysis for dimension reduction with weighted voting. The proposed system exhibits the analysis results of the credit card fraud data in the form of clusters for better visualization. This system is suitable for online applications which have memory or computation limitation.

Keywords: Credit card fraud detection, Data Mining, K-Means and PCA.

[1]. Gupta, Shalini, and R. Johari, "A New Framework for Credit Card Transactions Involving Mutual Authentication between Cardholder and Merchant", International Conference on Communication Systems and Network Technologies IEEE, pp: 22-26, 2011.
[2]. Y. Gmbh and K. G. Co, "Global online payment methods: Full-year", Tech Report published at "YSTATS.COM", pp: 2-15, 2017.
[3]. E. Duman, M. H. Ozcelik, "Detecting credit card fraud by genetic algorithm and scatter search", Expert Systems with Applications, 38(10), pp: 13057-13063, 2011.
[4]. Ju, W. H, and Vardi, Y, "A hybrid high-order Markov chain model for computer intrusion detection", Journal of Computational and Graphical Statistics, 10(2), pp: 277-295, 2001.
[5]. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey", ACM Computing Surveys, vol. 41, no. 3, pp: 15:1-15:58, 2009.

This article attempts to answer a much-doubted question through a graduate perspective of present and past scenario shipping industry and prediction of time for complete automation of it. Here, considerations made by other technological premonitory personnel, who have predicted that marine transportation as a whole will be completely automated is reviewed, and also their reasoning is taken forward for a prediction. It also takes various other factors into consideration, such as automation in other industries and how it will affect the automation in the Maritime and Logistics Industry. It mainly focuses on the technology present, and its proliferation and also the technology that is said to be present and implemented in the near future. The predicted time is considering the popularity of the implementation of certain technology. However, a lot of factors that cannot be converted into numbers are also taken into considerations, such as time taken by certain legislation to formulate laws to operate in an industry. All the other articles regarding this issue are reviewed, even considering web content made by others on the same.

Keywords: Maritime, Automation, Artificial Intelligence, Maritime vessel

[1]. http://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx
[2]. https://www.youtube.com/watch?v=j7RsRnYlz7I by TED; DEC 13, 2013
[3]. https://safety4sea.com/survey-future-is-autonomous-but-human-to-remain-key-component/ E-NAVIGATION ; SEP 14 2018
[4]. https://www.youtube.com/watch?v=DL0-yQFuz6M JeffHK; AUG 17, 2019

Underground drainage system (UDS) and the manholes in cities of our country India is one of the major issues, due to its poor maintenance. The drainage water contains sewage, domestic and industrial waste. When such thing gets blocked within sewers, UDS is overflowed on the roads causing water logging and sometimes gets mixed up in the drinking water which causes health hazards to common people. The sewers gases make manholes a lethal environment, when UDS requires maintenance. To overcome these issues, a model called "Smart Manhole Based UDS" is proposed here, which will monitor various parameters related UDS & manholes such as drainage fluid level and sewer gas level in the drainage system, manholes lid or cover conditions; location of the underground blockage by using Node MCU and the measured values will be stored in the cloud for future analysis. The real time monitored data will be analyzed and conditions of UDS and manholes will be sent to nearby municipal corporation office as emergency notifications through IoT systems.

Keywords: IoT- internet of things, UDS- underground drainage system, sewer gases, WSN- wireless sensor network, Node MCU- Node microcontroller unit, CPU- central processing unit, GUI- graphic user interface, RFID- radio frequency identification.

[1]. Thorkild Hvitved-Jacobsen, Jes Vollertsen, Asbjorn Haaning Nielsen, "Sewer Processes" CRC Press, by Taylor & Francis Group, LLC, 2013.
[2]. https://thewire.in/health/manhole-sewer-cleaning-india-manual-scavenging-sanitation
[3]. Mustafa Kocakulak and Ismail Butun, "An Overview of Wireless Sensor Networks Towards Internet of Things", 978-1-5090-4228-9/17/ IEEE, 2017.
[4]. Ephrem, E. (2015,June 8), "Architecture of Wireless Sensor Networks" Retrieved October 8, 2015,from http://servforu.blogspot.com.tr/2012/12/architecture-of-wirelesssensor- networks.html
[5]. J. Sathish Kumar, Dhiren R. Patel, Department of Computer Engineering, SVNIT, "A Survey on Internet of Things: Security and Privacy Issues" Volume 90 – No 11, March 2014.

The way that information is shared has always been revolved around knowledge sharing. Information is a value for the machine, but is described as an action requiring a reaction for the humans. Any gesture that can be visualized or sound that can be heard is used to convey information or instructions to a machine. The same action that can be stipulated by a machine evolves via machine learning by learning the predefined database or samples. Intelligence that evolves via machine learning is a derived product of an artificial intelligence (AI) that is bundled in a system via a software interface. This research area mainly deals with algorithms that need to access huge amounts of data and provide faster derivations and beyond. An algorithm implemented via a computer vision or a means of computation for processing such huge amount of data requires creation of a classifier............

Keywords: Hyper-spectral Image Processing, Remote Sensing, Machine Learning, Algorithm classifiers

[1]. E. Aptoula, M. C. Ozdemir, and B. Yanikoglu. Deep learning with attribute profiles for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 13(12):1970–1974, Dec 2016. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[2]. Naveen J.P. Anne, Amr H. Abd-Elrahman, David B. Lewis, and Nicole A. Hewitt. Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands. International Journal of Applied Earth Observation and Geoinformation, 33:47 – 56, 2014. K. Elissa, "Title of paper if known," unpublished.
[3]. A. Banerjee, P. Burlina, and C. Diehl. A support vector method for anomaly detection in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(8):2282–2291, Aug 2006. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, "Electron spectroscopy studies on magneto-optical media and plastic substrate interface," IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
[4]. Elfatih M. Abdel-Rahman, Fethi B. Ahmed, and Riyad Ismail. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. International Journal of Remote Sensing, 34(2):712–728, 2013.

Current space launch vehicles use chemical reactions (solid and liquid propellants) to achieve sufficient thrust to launch spacecraft. They carry a lot of fuel to escape the Earth‟s gravity. For long-distance space-travel, more fuel is required. But more fuel increases the weight of the spacecraft and to minimize fuel consumption, the need for fuel-efficient spacecraft is necessary. It can make the deep-space manned mission possible. The ratio of payload and the overall weight of spacecraft are uneven; that means, to carry one ton of mass in space we require more than 100 tons of launch mass. For space colonization, space rockets should be able to carry a higher percentage of human beings with it. Currently, due to the overall weight and design of the spacecraft, it is not possible to carry number of people. Through the above mentioned four techniques, we can save the fuel of spacecraft at a large extent and can reduce its fuel consumption.

Keywords: thrust, Spacecraft, fuel, deep-space

[1]. A. Kalam, "The Future of Space Exploration and Human Development.". August 2008, The Pardee Papers, Boston University
[2]. "Space Technology Grand Challenges," Dec. 2010, nasa.gov
[3]. "PROPELLANTS," history.nasa.gov, 2019. Available: https://history.nasa.gov/conghand/propelnt.htm.
[4]. Wikipedia Contributors, "Gas-generator cycle," Wikipedia, 2019. Available: https://en.wikipedia.org/wiki/Gas-generator_cycle.

―Different typesof medicalimaging technologies are used to view the human body in order to diagnose, monitor, or treat medical conditions‖. Each type of technology gives dissimilar information about the area of the body being studied or treated, related to possible disease, injury, or the effectiveness of medical treatment‖. This report embodies different basic imaging techniques along with recent trends in these techniques. This study also enables us to understand the historical background of these techniques and the future trends in imaging techniques being used for the benefit of humankind. Currently used techniques such as X-rays, CT scan, and MRI etc. are being used to investigate various ailments such as Bone fractures, Infections, Calcifications, some tumors, Arthritis in joints, Bone loss, Dental issues, and Heart problems. Recently, the focus of medical imaging research shifted toward.......

Keywords: CT Image, OCT Imaging, PET

[1]. Bushberg JT, Seibert JA, Leidholdt EM, Boone JM. The essential physics of medical imaging. Philadelphia: Lippincott Williams & Wilkins; 2002.
[2]. Kalender WA. Computed tomography: fundamentals, system technology, image quality, applications. Erlangen: Publicis; 2011. 3.Kak AC, Slaney M. Principles of computerized tomographic imaging. New York: IEEE Press; 1998. http://www.slaney.org/pct/pct-toc.html. Accessed Aug 2012.
[3]. Herman GT. Fundamentals of computerized tomography: image reconstruction from projections. Berlin: Springer; 2009.
[4]. Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance—physical principles and sequence design. New York: Wiley; 1999.
[5]. Hedrick WR, Hykes DL, Starchman DE. Ultrasound physics and instrumentation. St. Louis: Elsevier Mosby; 2005.

the design of "An Integrated smartphone based Dashboard for vehicle"is intended to developan integrated dashboard in automobile for smart cityapplication. The project involves the implementation of a proposed system used for successful real time data acquisition, fault diagnosis and display with child safety and vehicle monitoring features for any vehicle. Proper vehicle monitoring and maintenance can save time, money and improve the ownership experience. Our system is based on Microcontroller and Wireless Communication which is used to extract the vehicle's status or fault information, and then the results can be viewed to the driver to monitor various parameters like fuel consumption, engine temperature etc. for safe and careful driving. This project aims at developing an embedded system prototype for detecting the vehicle condition by monitoring the internal parameters collected from various sensors that are used in evaluating the vehicle"s current state............

Keywords: dashboard, real time monitoring, sensors, Bluetooth, voice control.

[1]. Shivanjali Thorat, Reshma Suryawanshi, Poonam Panage, G. R. Rahate, "An Integrated Digital Dashboard For Automobile System" Dept. of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering Pune, Maharashtra, India.
[2]. Mr.Saurav V. Malpani, Ms.Aishwarya D. Deshmukh, Ms.Gitanjali S. Sakore, Prof.Mrs.S.K.Bhatia, " Android Dashboard And Smart Vehicle Management", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 3, March 2015
[3]. P. Sivakumar, Jayashree Varadhan, N Ponnusamy, Rajaseeli Reginald, "Driver's intelligent dashboard and vision system for modern armoured fighting vehicles", Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2016 2nd International Conference on
[4]. XRS Corporation, "Omnitracs XRS Mobile ELD Android Operating System"
[5]. Rick Styll, "Fast Dashboards Anywhere with SAS® Visual Analytics", SAS Global Forum 2013

Digital image processing is the process of using computer algorithms to perform image processing on digital images. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing "a model of multidimensional systems". The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.

Keywords: Algorithms, sensor, array, acquisition.

[1]. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing", Indian edition published by Dorling Kindersley India Pvt. Ltd. 2009, Third Edition.
[2]. A. Deda, A. Samojedny, R. Koprowski, S. Wilczyoski, Z. Wróbel, (2013, Jun) Image analysis and processing methods in verifying the correctness of performing low-invasive esthetic medical procedures
[3]. A. El-Baz, A. Farag, M. El-Ghar, R. Fahmi, S. Yuksel, T. Eldiasty, W. Miller. A new CAD system for the evaluation of kidney diseases using DCE-MRI
[4]. A.I. El-Fallah, G.E. Ford (1997, May) Mean curvature evolution and surface area scaling in image filtering.
[5]. A. Hayat Gondal and M.N.A. Khan, A Review of Fully Automated Techniques for Brain Tumor Detection from MR Images [PDF].Available: http://www.mecs-press.org/ijmecs/ijmecs-v5-n2/IJMECS-V5-N2-8.pdf.

In the textile production, defect detection is an important factor on quality control process. The investment in automated texture defect detection becomes more economical reducing labor cost. The cost of fabric is often affected by the defects of fabrics that represent a major problem to the textile industry. Manual inspections have the problems as lack of accuracy and high time consumption where early and accurate fabric defect detection is an important phase of quality control. Therefore automate fabric inspection i.e. computer vision based inspection is required to reduce the drawbacks discussed above. Robust and efficient fabric defect detection algorithms are required to develop automated inspection techniques. From last two decades so many computer vision based methods have been proposed. This paper attempts to categorize and describe these algorithms. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features.

Keywords: fabric defect, automated visual inspection, quality control, defect detection, textile inspection

[1]. Zhang YF, Bresee RR, "Fabric defect detection and classification using image analysis." Journal of Textile research,1995, 65(1):1-9.
[2]. K.Shrinivasan, P. H. Dastoor, P. Radhakrishnaiah, and S. Jayaraman, "FDAS: A knowledge-based framework for analysis pf defects in woven textile structures", J. Textile inst., pt. 1, vol. 83, no.3,pp.431-448,1992
[3]. Henry Y. T.Ngan, Grantham K.H, Pang Nelson, H.C. yung, "Automated fabric defect detection- A review",Science Direct June 2011 vol.29, issue 7, Page no. 442-458, 2011
[4]. Arivazhagan S., Ganesan L. and Bama S. (2006), "Fault segmentation in fabric images using Gabor wavelet transform", International Journal of Machine Vision and Appications, Vol. 16, No.6, pp. 356-363
[5]. Kumar, "Computer-vision based fabric defect detection:A survay.", IEEE transactions on Industrial Electronics, vol.55,no.1, pp. 348-363, Jan.2008.

Due to the recent events of mass spreading of COVID-19, every physical operation has been hard stuck with its effect and has caused a sudden exponential increase in the usage of internet services. With everything going online and people using it for their respective activities, there has been a widespread of cyber bullies causing negative online behaviors, including comments that are rude, disrespectful or otherwise likely to make someone leave a discussion. The paper addresses the problem of classifying toxic comments into subcategories to help the online moderation. This paper has employed Multi-Label Classification using Problem Transformation methods.

Keywords: Multi-Label Classification, Binary Relevance, Classifier Chain, Machine Learning Algorithms.

[1]. Ojasvi Jain, Muskan Gupta, Sidh Satam, Siba Panda: "Has the COVID-19 pandemic affected the susceptibility to cyberbullying in India?", Computers in Human Behavior Reports, Volume 2, 2020, 100029, ISSN 2451-9588.
[2]. https://www.kaggle.com/c/jigsaw-toxic-comment-classification- challenge/data
[3]. Theodora Chu, Kylie Jue and Max Wang: "Comment Abuse Classification with Deep Learning",
[4]. https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2 762092.pdf
[5]. Kevin Khieu, Neha Narwal: "Detecting and Classifying Toxic Comments",