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Brain Tumor is a cluster of irregular cells which grows inner side of the brain by abandoned growth in tissues of the brain. Which wants to be treated, if left unprocessed it extent over a wide area in other parts of the brain too? It is very complicated and tough to detect the brain tumor in primary phase as there are very few or no symptoms in the beginning stages. Medical image processing and its segmentation is an active and attractive area for researchers. It has reached at the incredible place in diagnosing tumors after the discovery of CT and MRI. MRI is a valuable tool to detect the brain tumor and segmentation is performed to carry out the useful portion from an image. Detecting Brain tumor using Image Processing techniques involves four stages namely Image Pre-Processing, Image segmentation, Feature Extraction, and Classification.

Brain tumor, segmentation, MRI

[1] Abd-Ellah, Mahmoud Khaled, Ali Ismail Awad, Ashraf AM Khalaf, and Hesham FA Hamed. "Design and Implementation of a Computer-Aided Diagnosis System for Brain Tumor Classification” Sci.Int.(Lahore),27(3),2161-2163,2015. ISSN 1013-5316; CODEN: SINTE 8
[2] Mistry, Krupali D., and Bijal J. Talati. "Integrated approach for bone tumor detection from MRI scan imagery." In Signal and Information Processing (Icon SIP), International Conference on, pp. 1-5. IEEE, 2016.
[3] AMRUTA PRAMOD HEBLI, SUDHA GUPTA, “BRAIN TUMOR DETECTION USING IMAGE PROCESSING: A SURVEY”, International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982 Volume-5, Issue-1, Jan.-2017.
[4] Dipanjan Moitra, Dr. Rakesh Kumar Mandal “SEGMENTATION STRATEGY OF PETBRAIN TUMOR IMAGE” Indian Journal of Computer Science and Engineering (IJCSE), ISSN : 0976-5166 Vol. 4 No.5 Oct-Nov 2013
[5] Ahmmed, Rasel, and MdFoisal Hossain. "Tumor detection in brain MRI image using template based K-means and Fuzzy C-means clustering algorithm." In Computer Communication and Informatics (ICCCI), 2016 International Conference , IEEE, 2016.
6] Nooshin Nabizadeh,Miroslav Kubat,”Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features” Computers and Electrical Engineering, Elsevier 2015.
[7] Malathi R, Dr. Nadirabanu Kamal A R,” Brain Tumor Detection and Identification Using K-Means Clustering Technique”, Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, 27th March 2015.
[8] A.Sindhu1, S.Meera, “A Survey on Detecting Brain Tumorinmri Images Using Image Processing Techniques”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, ISSN (Print): 2320-9798, Vol. 3, Issue 1, January 2015.
[9] Ed-Edily Mohd. Azhari1, Muhd. Mudzakkir Mohd. Hatta1, Zaw Zaw Htike1* and Shoon Lei Win2 , “Brain Tumor Detection And Localization In Magnetic Resonance Imaging”. International Journal of Information Technology Convergence and Services (IJITCS) Vol.4, No.1, February 2014
[10] A.Harshavardhan, Dr. Suresh Babu,, Dr. T. Venugopal, “Analysis of Feature Extraction Methods for the Classification of Brain Tumor Detection”. International Journal of Pure and Applied Mathematics, Volume 117 No. 7 2017, 147-155, ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version).
[11] Rajeshwar Nalbalwar,Umakant Majhi ,Raj Patil,Prof.Sudhanshu Gonge,” Detection of Brain Tumor by using ANN” International Journal of Research in Advent Technology, Vol.2, No.4, April 2014 E-ISSN: 2321-9637.
[ 12] Parveen, Amritpal singh, “Detection of Brain Tumor in MRI Images, using Combination of Fuzzy C-Means and SVM” 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015.
[13] Moitra D. and Mandal R.” Review of Brain Tumor Detection using Pattern Recognition Techniques, Volume-5, Issue-2 E-ISSN: 2347-2693, 2017
[14] Rajeshwar Nalbalwar,Umakant Majhi ,Raj Patil,Prof.Sudhanshu Gonge, Detection of Brain Tumor by using ANN, International Journal of Research in Advent Technology, Vol.2, No.4, E-ISSN: 2321-9637, April 2014
[15] Madhumantee Naskar , “AN AUTOMATED SYSTEM FOR BRAIN TUMOR DETECTION& SEGMENTATION” Journal of Engineering Research and Studies E-ISSN0976-7916 ,J Engg Res Studies /Vol. VI/ Issue I/Jan.-March, 2015/03-05.
[16] Dharna, Priyanshu Tripathi, “ Brain Tumor Segmentation: A Review”, International Journal of scientific research and management (IJSRM) Volume 4 Issue 09 , 2016.
[17] Roopali R.Laddha, S.A.Ladhake, “A Review on Brain Tumor Detection Using Segmentation And Threshold Operations”, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014.
[18] Dharna, Priyanshu Tripathi, “ Brain Tumor Segmentation: A Review”, International Journal of scientific research and management (IJSRM) Volume 4 Issue 09 , 2016.
19] Bandana Sharma, Dr. Brij Mohan Singh, “Review Paper on Brain Tumor Detection Using Pattern Recognition Techniques” Bandana Sharma et al. International Journal of Recent Research Aspects ISSN: 2349-7688, Special Issue: Conscientious and Unimpeachable Technologies 2016.
[20] Ritu Rana1, Parvinder Singh2, “Brain Tumor Detection through MR Images: A Review of Literature”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 5, Ver. II (Sep. – Oct. 2015), PP 07-18

D. N. Lohare, R. Telgade, R. R. Manza, "Review Brain Tumor Detection Using Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.735-740, 2018.

This paper explores the technology of Augmented Reality by indulging in the first ever introduction to it, as well as how augmented reality has effected every individual’s day to day life now and how it will affect every individual’s day to day life in the future. It ranges from the first occurrence of augmented reality as a French author’s idea to its establishment as a technology which is not only taking over the world like a storm but will gradually make it impossible to imagine functioning without the technology of augmented reality which will be deeply rooted in our lifestyles . It is discussed how augmented reality is becoming an irreplaceable part of industries as well as people. The three approaches to augmented reality are mentioned with the concept of their working. The future of augmented reality is discussed focussing on various sectors like marketing, designing, safety and security, shopping, social media, etc.

Augmented Reality, First Occurrence, Irreplaceable Part, Approaches to Technology, Future

[1] Metz, Rachel. “Augmented Reality is Finally Getting Real”. MIT Technology Review. N.p., 2016. Web. 24 June 2016.
[2] S. C.-Y. Yuen, G. Yaoyuneyong and E.Johnson, “Augmented Reality: An Overview and Five Directions for AR in Education,” Journal of Education Technology Development and Exchange, Vol. 4, no. 1, pp. 119-139.
[3] Nivedha.S1, Hemalatha.S2 “A Survey on Augmented Reality”, International Research Journal of Engineering and Technology, 2015.
[4] Miss. Arti Yadav, Miss. Taslim Shaikh, Mr. Abdul Samad Hujare, Prof. Murkute P.K.(Guide) “A Survey on interior Design using Augmented reality”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 5, May 2015
[5] Simonite, Tom. “Augmented Reality Meets Gesture Recognition”. MIT Technology Review. N.p., 2016. Web. 18 May 2016.
[6] Ronald Azuma, Yohan Baillot, Reinhold Behringer, Steven Feiner, Simon julier, Blair MacIntyre “Recent Advances in Augmented Reality”, Computers & Graphics, November 2001K. Elissa, “Title of paper if known,” unpublished.
[7] Pooja Chawla, “Using Augmented Reality for Setting Furniture”, IJSCE,Volume 5, E-ISSN: 2456-3307,pp.1-2, April 2018.
[8] Neetu Sharma, “Augmented Reality as Data Retrieval”, IJSCE,Volume 3, E-ISSN: 2456-3307,pp.2-3, March 2018.

Khushi Prasad, Rizwan Khan, "A Review paper on Augmented Reality," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.741-743, 2018.

Time slot allocation concept for cognitive cellular hybrid network (CCHN) is proposed in this paper. It has been addressed an efficient resource allocation scheme by mitigating interference problem in CCHN. A combination of hybrid underlay-overlay network is introduced to overcome the limitations of underlay and overlay cognitive network. We have adopted an opportunistic cooperative sensing based spectrum access scheme to allocate resource as available time slots among multiple cognitive radio (CR) users. Particle swarm optimization (PSO) algorithm has been implemented to optimize transmitting parameters such as antenna size, modulation index, transmission rate, SNR. In order to achieve the maximum network capacity and proper distribution of time slots beam forming concept of PSO has been utilized. As a result, a new scheme for time slot allocation in cognitive hybrid networks has been adopted. A mathematical model and emulated results have been presented to justify the proposed scheme. The graphical analysis reviles the improvement of throughput performance, system efficiency, transmission rate and the quality of service (QoS) for both the primary and secondary users.

Cognitive radio, Cooperative sensing, Hybrid network, PSO, Outage probability

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P.Joarder, S.Chatterjee, "Resource Allocation in Cognitive Cellular Hybrid Network Using Particle Swarm Optimization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.744-749, 2018.

Phishing sites are manufactured or spurious URLs that are made by malignant people to imitate or imitate URLs of genuine URLs. An extensive bit of these sorts of URLs have most elevated twin to trap their casualties for tricks. Unwary Web customers may be successfully betrayed by this kind of trick. The effect is the break of data security through the exchange of private information and the losses may encounter the bad effects of financial losses and more example hacking. In this paper detection of phishing URLs is done by using Bayes net and Naïve Bayes algorithm and evaluation of risk regarding phishing URLs is done with the help of attributable risk. A training dataset of 1800 URLs (containing 1080 legitimate and 720 phished URLs) has been made to train the algorithms. Testing dataset of 720 URLs (containing 288 legitimate and 432 phished URLs) is used for making predictions using the DAG model classifier which is generated after the training of Bayes Net and Naïve Bayes Algorithm. True negative rate, True positive rate, false negative rate, false positive rate, Error rate and Accuracy are calculated after testing dataset by DAG classifier. Result shows Bayes Net has an accuracy of 71.3% and the Naïve Bayes has an accuracy of 80.5% and calculation of risk is done on the basis of attributable risk. If risk percentage for the URLs attributes is greater than 80% then risk is high, if it is between 50-80% then risk is medium and below 50% risk is low.

Attributable Risk, Bayes Net, Naïve Bayes, Phishing, Risk Assessment

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Priya Raj, Meenakshi Mittal, "Detection of Phishing URLs using Bayes Net and Naïve Bayes and evaluating the risk assessment using Attributable Risk," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.750-755, 2018.

Conservation of energy is an important aspect that is to be considered while planning a resource utilizing project. Vitality proficient lighting framework is a stage towards an asset productive and helps to maintain sustainable growth. If pedestrians or automobiles, summarized as active traffic elements, are not situated near street lights, it is not vitality proficient to turn them on when the light intensity is higher in surrounding conditions. The basic plan is to switch on street lights only when they are required to the traffic elements thus fulfilling the need of illumination at dark places. The system also allows monitoring of the lights thus checking their proper functioning at all times. A mechanized lighting or Light on Demand (LoD) framework is required to execute moving light. By automating the street light system, light naturally switches on/off in halfway street areas. A considerable amount of energy is conserved by the system and proper illumination of street lights has been achieved with this designed model.

Light on demand, Short Message Service, Light Emitting Diode, Iterative Dichotomiser

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Harshita Gupta, Naman Tripathi, Prasad Thorve, Vina M. Lomte, "Energy Efficient Smart Street Light System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.756-760, 2018.

In Data mining field, frequent item set mining is one of the most intensively investigated problems in terms of computational complexity. The concept is widely used in market basket analysis, finance, and health care systems. Finding frequent patterns plays an essential role in mining associations, correlations and much other interesting relationship among data. The interest in the problem still persists despite of elaborate research conducted in the last two decades, due to its computational complexity and the fact that the results sets can be exponentially large. This combinatorial explosion of frequent item set methods become even more problematic when they are applied to Big Data. In this survey paper, an effort is made to present various popular algorithms and its analysis.

Frequent item set mining, Association rule mining, Big Data

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[8] Jianlong Gu, Baojin Wang , Fengyu Zhang, Weiming Wang, and Ming Gao “An Improved Apriori Algorithm” in the International Conference on Applied Informatics and Communication ICAIC 2011:Applied Informatics and Communication pp 127-133
[9] Cheung, D-W., Han, J., Ng, V-T., Wang, C-Y, “Maintenance of Discovered Association Rules in Large Databases : An Incremental Update technique” in the 12th International Conference on Data Engineering, New Orleans, LA., 26 February-1 March 1996,pp. 106-114.
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[17] Akilandeswari. S, A.V.Senthil Kumar, “A Novel Low Utility Based Infrequent Weighted Itemset Mining Approach Using Frequent Pattern”,International Journal of Computer Sciences and Engineering(IJCSE),Vol.3,Issue 7,pp.181-185,2015.
[18] R.B.M. Sayyad,P.S. Yalagi, “Infrequent Weighted Itemset Mining for Large Dataset” International Journal of Computer Sciences and Engineering(IJCSE),Vol.5,Issue 6,pp.149-153, 2017.

Uma.N, Prashanth C.S.R, "A Survey and comparative study of the various algorithms for Frequent Itemset Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.761-765, 2018.

This paper presents an edge based embedding technique to achieve undetectability in steganography with minimal amount of distortion. In this method, desired information is embedded in the edges, as the edges are least susceptible to Human Visual System (HVS) and therefore, remains concealed. Further, the proposed technique uses Majority Parity Check to improve the PSNR of image. The prominent issue with steganography is the amount of distortion caused due to embedding. The proposed technique offers the flexibility of attaining high security with minimal distortion. Experimental results show that the technique provides reduction in the distortion by 20% in comparison to other well-known techniques available in literature.

Steganography, Edge Adaptive, LSB, Majority Parity Check

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Neha Singla, Khushil Kumar Saini, "An Efficient and Secure Steganography Technique Using Edge Adaptive Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.766-772, 2018.

This paper aims to introduce a technique concept for facilitating communication between deaf & dumb and normal people. We propose a system which helps in translating speech/text into equivalent hand gestures. This can be done by image processing and/or animation techniques. With this application we hope to reduce the communication gap between ‗deaf & dumb‘ and normal people by eliminating the need of a human translator.

Hand gesture, Gesture Animation , Hand model, Finger pose estimation

[1]. M . K . Bhuyan , V . Venkata Ramaraju , Yuji Iwahori Hand gesture recognition and animation for local hand motions 21st March, 2013.
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Bidit Hazarika, Dipankar Das, Dibyajyoti Changkakoti, Abhinov Deka, Adarsh Pradhan, "Hand Gesture Animation Model for Local Hand Motion," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.773-779, 2018.

Motion Detection surveillance system is an advancement in the current era of technology. In this paper, we have proposed a technology to detect objects, to track moving objects, to eliminate noises and to save videos of our interest. This technique will be highly useful for both home and office needs as these areas demand a reliable surveillance system nowadays. The experiments conducted in accordance with proposed methods which are suitable for real-time surveillance system [1]. The main objective of this provide a secure surveillance system in areas such as ATMs or offices where recording takes place only when objects is present (in motion).

Background Subtraction; Robust Algorithm; Gaussian Mixture Models; Multiple Moving Object Tracking; Background Modeling; Blob Labeling; Group Tracking

[1] Li Fang, School of Computer Science Nanyang Technological University, Singapore and Zhang Meng, Dunman High School Claire Chen Qian Hui River Valley High School, Singapore. “Smart Motion Detection Surveillance System”. Published by IEEE on 21 July 2009.
[2] M. Hedayati, Wan Mimi Diyana Wan Zaki and Aini Hussain, Department of Electrical, Electronics and Systems Engineering Faculty of Engineering University Kebangsaan Malaysia, “Real-Time Background Subtraction for Video Surveillance: From Research To Reality”, 2010, 6th International Colloquium on Signal Processing & its Applications (CSPA), IEEE publication, 09 August 2010.
[3] Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo, “Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems” IEEE Transactions on Consumer Electronics, Vol. 57, No. 3, August 2011. :1165 – 1170.
[4] Fang Zhu, Zhangjun Fei and Feiling Chen, “A Fast and Robust Algorithm of Motion Detection for distributed outdoor surveillance”, National ASCI System Engineering Research Center, South East University Nanjing, P.R.China and Surveillance Engineering Research Center Nanjing, P.R.China.
[5] Mr. Deepjoy Das and Dr. Sarat Saharia from Department of Computer Science & Engineering, Tezpur University, Assam, India “Implementation and Performance Evaluation Of Background Subtraction Algorithms” International Journal on Computational Sciences & Application (IJCSA) Vol 4, No. 2, April 2014.
[6] Amrut C. Saindane, Pravin S. Patil from North Maharastra University, Jalgaon “An Efficient Human Recognition Using Background Subtraction and Bounding Box Technique for Surveillance Systems” IJCSE Publisher volume-4, Issue-12.
[7] Aseema Mohanty, Scholar, DIMAT, CSVTU Raipur, India and Sanjivani Shantaiya, Assistant Professor, DIMAT, CSVTU, Raipur, India, “A Survey On Moving Object Detection using Background subtraction Method in video”, IJCA Proceedings on National Conference On Knowledge, Innovation in Technology and Engineering (NCKITE 2015(2)), Vol 182, 5-10 July 2015 Edition.
[8] Rupali S.Rakibe, Bharati D.Patil from GHRCEM Wagholi, Pune. “Background Subtraction Algorithm Based Human Motion Detection”, International Journal of Scientific and Research Publication, Volume 3, Issue 5 May 2013.
[9] Hájek Lukás, “Czech Technical University in Prague Faculty of Nuclear Sciences and Physical Engineering”, 3 April 2012.

Yash Gupta, Samriddhi Mishra, Deepti Sharma, "Smart Surveillance System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.780-784, 2018.

This paper presents primarily on a low cost fire search and control system based on smoke and heat identification proposed. It has a combination of electrical / electronic devices / devices working together to identify keeping people through audio or visual media after detecting fire and presence. Fire alarms are used in the context of fire or fire drill. They manually or automatically activated. After fire protection goals are established-usually by referring to the minimum level appropriate model building code, mandatory protection by insurance agencies, and other officials - the Fire Alarm takes on the designer details specific parts, arrangements, and required interfaces to achieve these goals [1]. These sets out various circuits for the paper spot the fire. These alarms can be activated smoke detectors or hot detectors, which detects the fire. Then, it will automatically use a relay send short message service (SMS) to registered mobile numbers. Switch water scratch or solenoid pump spraying water or fire spraying.

Alarm, Fire Detection, Audio or Visual Medium, Relay

[1] Chenebert, A., Breckon, T.P., Gaszczak, A. (September 2011). "A Non-temporal Texture Driven Approach to Real-time Fire Detection ".Proc. International Conference on Image Processing. IEEE. pp. 1781–1784. doi:10.1109/ICIP.2011.6115796.
[2] www.fire.org.nz/...Fire...Alarms/.
[3] Huide Liu ; Lili Gao ; Suwei Li ; Tao Wu “ About automatic fire alarm systems research”, The 2nd IEEE International Conference on Information Management and Engineering (ICIME), 16-18 April 2010, pp. 419 – 421.
[4] Lei Zhang, and Gaofeng Wang, “Design and Implementation of Automatic Fire Alarm System based on Wireless Sensor Networks”,
[5] Proceedings of the 2009 International Symposium on Information Processing (ISIP’09) Huangshan, P. R. China, August 21-23, 2009, pp. 410-413
[6] Omar Asif, Md. Belayat Hossain, Mamun Hasan, Mir ToufikurRahman, Muhammad E. H. Chowdhury, “Fire-Detectors Review and Design of an Automated, Quick Responsive Fire-Alarm System Based on SMS”, Scientific Research Publishing Inc, 28th August 2014.
[7] www.systemsensor.com.
[8] Zhang, L. and Wang, G. (2009) Design and Implementation of Automatic Fire Alarm System Based on Wireless Sensor Networks. Proceedings of the International Symposium on Information Processing (ISIP’09), Huangshan, 21-23 August 2009, 410-413.
[9] Kwon, O.H., Cho, S.M. And Hwang, S.M. (2008) Design and Implementation of Fire Detection System. Advanced Software Engineering and Its Applications, Hainan Island, 13-15 December 2008, 233-236.
[10] Li, J.H., Zou, X.H. and Lu, W. (2012) The Design and Implementation of Fire Smoke Detection System Based on FPGA. Proceedings of the 24th Control and Decision Conference, Taiyuan, 23-25 May 2012, 3919-3922.
[11] Cote, A. and Bugbee, P. (1988) Ionization Smoke Detectors. Principles of Fire Protection. National Fire Protection Association, Quincy, 249.
[12] Northeast Document Conservation Center, Nick Artim, an Introduction to Fire Detection, Alarm, and Automatic Fire Sprinklers. http://www.nedcc.org/free-resources/preservation-leaflets/3.-emergency-management/3.2-an-introduction-to-fire-detection,-alarm,-and-automatic-fire-sprinklers.
[13] Wikipedia, the Free Encyclopaedia (2013) Flame Detector. http://en.wikipedia.org/wiki/Flame_detector
[14]Wikipedia, the Free Encyclopaedia (2013) Flame Detection. http://en.wikipedia.org/wiki/Flame_detection.
[15] Figaro Engineering Inc. (2014) http://www.figarosensor.com/products/general.pdf .

S. Narsingh Rao and K. Malakondiah, "Review Paper on Microcontroller Based Fire Detection Alarm System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.785-787, 2018.