Volume-6 , Issue-7 , Jul - 2018 Go Back

Segmenting a broad class of histological structures is a prerequisite to determine the pathological basis of cancer, to clarify spatial interactions between histological structures in the tumor microenvironments, making precision medicine studies easy with deep molecular profiles, and provide an exploratory tool for pathologists. Histological structure determination helps elucidate spatial tumor biology. Role focuses on the segmentation of histological structures present in colored images with stains (H & E) of the breast tissue. Accurate segmentation of histological structures can help build a spatial interaction map to serve as an exploratory tool for pathologists. Breast cancer if detected early can be cured easily. Hence detection methods need to have more accurate diagnosis. Images obtained out of the scans done, processed to get the segments, which are then seen as clusters. These clusters are evaluated with classification techniques to reach the diagnosis result.

histopathalogical image analysis, image segmentation, image statistics

[1] Luong Nguyen, “Spatial statistics for segmenting histological structures in HE stained tissue images", IEEE TMI, 2017.
[2] E. Bejnordi et al, “Automated detection of dcis in whole-slide he stained breast histopathology images", IEEE TMI, 2016.
[3] J. Vicory et al, “Appearance normalization of histology slides, Computerized Medical Imaging and Graphics",vol. 43, pp. 8998, 2015.
[4] X. Li and K. N. Plataniotis, “A complete color normalization approach to histopathology images using color cues computed from saturation weighted statistics"IEEE TBME, vol. 62, no. 7, pp.18621873, 2015.
[5] P. Isola et al, “Crisp boundary detection using pointwise mutual information,"ECCV 2014, 2014, pp. 799814.
[6] Vahadane et al, “Structure-preserving color normalization and sparse stain separation for histological images,"IEEE TMI, 2016.
[7] F. Liu and L. Yang, “A novel cell detection method using deep convolutional neural network and maximum-weight independent set,"MICCAI, pp. 349357, 2015.
[8] J. L. Fine, “21st century workow: A proposal,"Journal of Pathology Informatics, vol. 5, no. 1,p. 44, 2014.
[9] B.-R. Wei and R. M. Simpson, “Digital pathology and image analysis augment biospecimen annotation and biobank quality assurance harmonization,",Clinical biochemistry, vol. 47, no. 4, pp.274279, 2014
[10] M. T. McCann et al, “Images as occlusions of textures: a framework for segmentation,", Clinical biochemistry, vol. 47, no. 4, pp. 274279, 2014.
[11] N. M. Rajpoot, “HyMaP: A hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images,",Journal of Pathology Informatics, vol.4,no.2,p.1,2013
[12] S. Tamilarasan, P.K. Sharma, “A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling”, International Journal of Computer Sciences and Engineering, Vol.5, No.1, pp.53-59, 2017.

S.B.Pawar, V.S.Gaikwad, "Segmentation and Classification of Histologocal Structures in H & E Stained Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.869-873, 2018.

We are witnessing an exponential growth of the Internet of Things (IoT) market. Across the globe, wherever there is internet connectivity, each individual owns multiple devices which are connected to the internet. Further, there are “things” which are directly connected to each other through the internet and act independent of human intervention by participating in automated business processes to provide business value. A wide range of IoT applications have been developed and deployed in the recent past. However, the IoT technologies have some inherent drawbacks and challenges, for e.g. security and scalability issues. On the other hand the use of blockchain technologies is also experiencing an exponential growth, even beyond the financial applications. As both IoT and blockchain technologies are finding widespread acceptance, researchers and practitioners have experimented with the adoption of blockchain technologies for IoT based applications with a view to build robust and state of the art systems. Such initiatives have been quite successful. This paper reviews the current research status of IoT and blockchain technologies and discusses how the blockchain technologies are leveraged to address some of the challenges faced during the development of IoT based applications. It identifies research trends and future scope to build IoT architectures based on blockchain technologies.

IOT, Blockchain, Security, Distributed Ledger, Sensors, Actuators, SOA, Reference Architecture, Ethereum, Raspberry Pi, Scalability, Digital Transformation

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[2] The Internet of Things: an Overview, Internet society, October 2015.
[3] Bagadhi Sateesh. “Introduction to Internet of Things”, International Journal of Computer Sciences and Engineering. Vol.-6, Issue-6, June 2018.
[4] Gurpreet Kaur1 and Manreet Sohal. “IOT Survey: The Phase Changer in Healthcare Industry”, Int. J. Sc. Res. in Network Security and Communication (IJSRNSC), Volume-6, Issue-2, April 2018.ISSN: 2321-3256.
[5] The Industrial Internet of Things Volume G1: Reference Architecture. 2017. IIC:PUB:G1:V1.80:20170131.
[6] Joss Colchester, “Blockchain Overview”, A Complexity Labs Publication eBook, 2018.
[7] Yuanyu Zhang,Shoji Kasahara,Yulong Shen, Xiaohong Jiang and Jianxiong Wan. “Smart Contract-Based Access Control for the Internet of Things”, arXiv:1802.04410v1 [cs.CR] 13 Feb 2018.
[8] “What is Blockchain Technology? A Step-by-Step Guide For Beginners”, blockgeeks.com.
[9] Chandrashekhar Singh Rajawat and Hemant Gupta. “Smart Contract Management Using Blockchain Technology”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Volume 3, Issue 3, ISSN : 2456-3307.
[10] Kshetri, Nir (2017). "Can Blockchain Strengthen the Internet of Things?”, IEEE IT Professional (19)4, 68-72
[11] Harsh Agrawal, “IoT, Blockchain and the Future”, DZone, IoTZone, Mar. 07, 18.
[12] Jeremy Coward, “Meet the Visionary Who Brought Blockchain to the Industrial IoT”, Internet of Things Institute, 13 Dec. 2016.
[13] Darth Revan, Blockchain, “Ethereum & IoT POC: Machine Maintenance”, Series of posts in Medium. Nov 6, 2017 to Nov 27, 2017.
[14] Said Eloudrhiri. “Create a private Ethereum blockchain with IoT devices”, Series of tutorials in ChainSkills. 24 February 2017 to 3 April 2017.
[15] Marco Conoscenti, Antonio Vetr`o and Juan Carlos De Martin. “Blockchain for the Internet of Things: a Systematic Literature Review”, IEEE author’s version of the work.
[16] Kambhampaty, Shankar., Chandra, Satish., (2006), “Enterprise Architecture Definition Framework for IT Service Providers”, International Federation for Information Processing, Volume 205, Research and Practical Issues of Enterprise Information Systems, eds. Tjoa, A.M., Xu, L., Chaudhry, S., (Boston:Springer), pp.261-272.
[17] Kambhampaty, Shankar., Chandra, Satish., “Service Oriented Architecture for Enterprise Applications”, Proceedings of the 5th WSEAS Int. Conf. on Software Engineering, Parallel and Distributed Systems, Madrid, Spain, February 15-17, 2006 (pp48-54).

Satish Chandra Gullena, "IoT Architectures based on Blockchain Technologies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.874-878, 2018.

Steganography relies on volatility in order to perform information hiding inside apparently innocuous payloads. Steganography helps in create secure communication between two parties, without any mediator noticing the presence of the particular communication. Steganalysis is the parallel to steganography. A steganalyst tries to find out the presence of a covert communication between two parties and either modify their communication. Many different techniques are introduced to make communication more secure by using steganography. All of them are proven to be secure in there own way. Many new techniques are coming in existence from past few years. In this paper, we review the different techniques that has already introduced.

Steganography, QR code, DWT, Bluetooth, Logistic Map , DNA

[1]. Rutuja Kakade, Nikita Kasar, Shruti Kulkarni, Shubham Kumbalpuri, Sonali Patil, Student, Associate Professor, Dept.of Computer Engineering, PCCOE, Maharashtra, India” Image Steganography and Data hiding in QR Code” International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 05 | May -2017, e-ISSN: 2395 -0056.
[2]. Divya Suryawanshi, Meetali Salvi, Soumya Pandey Terna Engineering College, Nerul, Navi Mumbai IT Department, Mumbai University, Image steganography for criminal cases, Volume 5, Issue 2 | ISSN: 2321-9939
[3]. Marwan Ali Albahar, Olayemi Olawumi, Keijo Haataja, Pekka Toivanen University of Eastern Finland, School of Computing, Kuopio Campus, “ A novel method for bluetooth pairing using steganography”International Journal on Information Technologies & Security, № 1 (vol. 9), 2017
[4]. Shuliang Sun,School of Electronics and Information Engineering, Fuqing Branch of Fujian Normal University, China,” A Novel Secure Image Steganography Using Improved Logistic Map and DNA Techniques”
https://www.researchgate.net/publication/317829911, Article • May 2017.
[5]. S. U. Maheswari and D. J. Hemanth, “Different methodology for image steganography-based data hiding: Review paper,” International Journal of Information and Communication Technology, vol. 7, issue 4/5, pp. 521-536, 2015.
[6]. X. Wangand and D. Luan, “A Novel Image Encryption Algorithm Using Chaos and Reversible Cellular Automata, Communications in Nonlinear Science and Numerical Simulation, Vol. 18, No. 11, pp. 3075-3085, November, 2013.
[7]. S.K. Yadav, Manish Dixit Dept.of Computer Engineering, Madhav Institute of technology and science, Gwalior, India “A comparative study for image steganography using Transformation Domain method” International journal of science and Engineering(IJCSE), Volume: 05 Issue: 06 | Jun -2017.
[8]. Rajesh Shah, Yashwant Singh Chouhan Christian eminent college, Indore (M. P.)India” Encoding of Hindi Text Using Steganography Technique” International Journal of Scientific Research in Comp c Research in Computer Science and Engineering Science and Engineering Science and Engineering Research Paper Vol-2, Issue-1 E-ISSN: 2320-7639

Kirti, Parmod Sarowa, "Current Trends In Steganography," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.879-882, 2018.

The Internet of Things evolved in various application areas that include medical care or health care. This technology helps the patients and doctors to predict the various diseases accurately and diagnose these diseases according to result. The important aspect of this survey is how data collected by sensor-enabled devices in healthcare or medical care environment of the Internet of Things is processed and classified. This survey paper provides a recent review of the different classification algorithms such as SVM, Naïve Byes, Decision Tree, KNN etc. which were used to classify the data collected from sensor-enabled devices of healthcare or medical care environment of the Internet of Things by the help of comparison table. This survey shows a brief review of how IoT gives their contribution in the field of healthcare by using different sensors and communication protocols. This paper also outlines the parameters of classification algorithms such as in terms of accuracy, true positive rate (TPR), precision, false positive rate (FPR) etc used to classify the healthcare data.

Internet of Things, Large Data Set, Healthcare, Medical care, Classification Algorithms, Smart Healthcare

[1] S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain and K. S. Kwak, "The Internet of Things for Health Care: A Comprehensive Survey," in IEEE Access, vol. 3, pp. 678-708, 2015.
[2] K. Chui, W. Alhalabi, S. Pang, P. Pablos, R. Liu, and M. Zhao, “Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications,” Sustainability, vol. 9, no. 12, p. 2309, Dec. 2017.
[3] Gowrishankar S., Prachita M Y. and Arvind Prakash, “IoT based Heart Attack Detection, Heart Rate and Temperature Monitor”. International Journal of Computer Applications (IJCA) 170(5):26-30, July 2017
[4] I. Bisio, A. Delfino, F. Lavagetto and A. Sciarrone, "Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition," in IEEE Internet of Things Journal, vol. 4, no. 1, pp. 135-146, Feb. 2017.
[5] Marimuthu Palaniswami, Rajkumar Buyya, Jayavardhana Guddi, Slaven, Marusic, “Internet of Things (IOT): A Vision, Architectural Elements, And Future Directions,” Elsevier, Future Generation Computer Systems, vol.29, pp. 1645-1660, Feb. 2013.
[6] Furqan Alam, Rashid Mehmood, Iyad Katib, Aiiad Albeshri, “Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)”, Procedia Computer Science, Volume 98, 2016, Pages 437-442, ISSN 1877-0509.
[7] Feng Chen, , Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, Xiaohui Rong, “Data Mining for the Internet of Things: Literature Review and Challenges”, International Journal of Distributed Sensor Networks(IJDSN), 2015, Volume 2015.
[8] Prabal Verma, Sandeep K. Sood, “Cloud-Centric Iot Based Disease Diagnosis Healthcare Framework”, Journal of Parallel and Distributed Computing (JPDC), 2017.
[9] Bagadhi Sateesh, "Introduction to Internet of Things", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1086-1090, 2018.
[10] Mantripatjit Kaur, Anjum Mohd Aslam, "Big Data Analytics on IOT: Challenges, Open Research Issues and Tools", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018
[11] Syed L., Jabeen S., Manimala S., “Telemammography: A Novel Approach for Early Detection of Breast Cancer through Wavelets Based Image Processing and Machine Learning Techniques”. Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham, 2018.
[12] Verma, P., Sood, S.K. & Kalra, S., “Cloud‑Centric IoT Based Student Healthcare Monitoring Framework”, Journal of Ambient Intelligence and Humanized Computing (JAIHC), (2017).
[13] Sanjay Sareen, Sandeep K. Sood, Sunil Kumar Gupta, “IoT-based cloud framework to control Ebola virus outbreak”, Journal of Ambient Intelligence and Humanized Computing, Springer 2016.
[14] N. Keshan, P. V. Parimi and I. Bichindaritz, "Machine learning for stress detection from ECG signals in automobile drivers," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 2661-2669.
[15] P. S. Pandey, "Machine Learning and IoT for prediction and detection of stress," 2017 17th International Conference on Computational Science and Its Applications (ICCSA), Trieste, 2017, pp. 1-5.
[16] A. Walinjkar and J. Woods, "ECG classification and prognostic approach towards personalized healthcare," 2017 International Conference On Social Media, Wearable And Web Analytics (Social Media) (ICWWA), London, 2017, pp. 1-8.
[17] Ani R, Krishna S, Anju N, Sona Aslam M,O.S Deepa, “IoT Based Patient Monitoring and Diagnostic Prediction Tool using Ensemble Classifier”, International Conference on .
[18] D. Azariadi, V. Tsoutsouras, S. Xydis and D. Soudris, "ECG signal analysis and arrhythmia detection on IoT wearable medical devices," 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, 2016, pp. 1-4.

Akhil Bansal, Manish Kumar Ahirwar, Piyush Kumar Shukla, "A Survey on Classification Algorithms Used in Healthcare Environment of the Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.883-887, 2018.

Edge detection is a crucial step in medical imaging and in a no. of other image processing applications, such as face-identification or recognition, and other classification problems. Various methods have been developed for edge detection based on applications and edge types. Some of the most common techniques used are Sobel, Prewitt, Robert, LoG and Canny etc. However, most of these methods for edge detection of various images (including x-rays image) is a computationally expensive process in terms of both time and space. Because of this delay the patients and the doctors do not get instant information or imaging reports (for example regarding fractured bone in case of x-rays). This ultimately leads to delayed diagnosis and treatment of the patient. In this work we present our findings of research related to an important edge detection technique which involve finding image gradient. We emphasize that our approach is equally valid for many different kinds of edges in an image and not just for fractured bone. To eliminate latency issue we used a graphical processor with CUDA API to implement an image gradient. The graphical processors are massively parallel processors that come inside a graphics card and have become a standard piece of hardware on all modern day computing systems including portable hand-held device. We emphasize that alternate solutions such as FPGA (Field Programmable Gate Array) and ASIC (Application Specific Integrated Circuit) based solutions are much costlier and take much longer time for development as compared to a graphical processor which is programmable using C-CUDA. We compared our implementation’s performance with respect to a CPU-only implementation. To prove our idea we used an algorithm which is a parallel version of naïve serial algorithm. Thanks to GPU’s enormous amount of computational units, our GPU-implementation shows several fold speed ups with respect to a standard CPU-only implementation. Our proof-of-concept (PoC) developed as part of this research, thus establish that the GPU stands a very good candidate for such edge detection problems where we need faster results, i.e. in real time or in near real-time.

Digital Image, Edge detection, GPU, CUDA, X-ray, gradient

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Saima Iram, Jabir Ali, Pradeep Kumar, "Performance Enhancement of Edge Detection Methods for Human Bone Fracture X-Ray Image Using Graphical Processors," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.888-894, 2018.

A wireless sensor network made up of huge amount of devices that are less in size as well as self-directed and distributed. These devices are known as sensor nodes that comprises of various components. Sensor nodes are low power battery devices. Energy consumption is a vital issue in wireless sensor networks (WSN).Various MAC protocols have been designed to lower the usage of energy in WSN. WSN-MAC protocols are categorized into two types: contention based and schedule based protocols. This paper discusses the challenges of WSN and Schedule - based MAC protocols and compare the various schedule based MAC protocols by highlighting their strengths and weaknesses.

Time division multiple access (TDMA); Wireless Sensor Network (WSN); MAC protocols

[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci(Aug. 2002),” A survey on sensor networks”, IEEE Communications Magazine, vol.40, no.8,pp. 102-114.
[2] Carlo Fischione,”An Introduction to Wireless Sensor Networks”, Royal Institute of Technology,page no. 69.
[3] D. Estrin, D. Culler, K. Pister, and G. Sukhatme(Jan 2002,), “Connecting the physical world with pervasive networks”, IEEE Pervasive Computing, pp. 59-69.
[4] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci(Mar.2002), “ Wireless sensor networks : A survey”, Computer Networks, vol 38, no. 4 , pp. 393-422.
[5] F. Zhao and L. Guibas, Wireless Sensor Networks (2004): An Information Processing Approach, Morgan Kaufmann Publishers, San Francisco, CA.
[6] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J. Anderson(Sept. 2002), “ Wireless sensor networks for habitat monitoring”, in Proceedings of 1st ACM International Workshop on Wireless sensor networks and Applications (WSNA’ 02), Atlanta, GA, ,pp. 88 -97.
[7] Abul Kalam Azad, M. Humayun Kabir, Md. Bellal Hossain (November 2013.) ,” A Survey on Schedule based MAC protocols for Wireless Sensor Networks”,International journal of Computer Science and Network, Vol. 2 ,Issue 6.
[8] W.ye, J. Heidemann and D. Estrin, “An Energy efficient MAC protocol for wireless sensor Networks”, IEEE INFOCOM,New York,vol. 2, June 2002.
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Neetu, Sanjeev Indora, "Schedule based MAC Protocols in wireless sensor networks - A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.895-901, 2018.

Now a day’s, Pressure to relinquish the software is superseding the software testing. A project or product is being relinquished without concentrating on testing. This is transpiring to capture the market from the competitors. Hence its often a best practice across different organizations QA is verbalized to be a supplemental or overhead to the organization. No matter whatever the stake holder’s defining the QA, it’s the project manager or the Quality manager’s job not to make it a very extravagant paper weight. In general, QA is not a sumptuous area being one of the phases in Software Project management. QA turns extravagant only if it is neglected or ignored. This paper discusses about different techniques, how better they could be taken care w.r.to Quality. These methods avail a Project or Quality manager to work on QA for not making it as a very sumptuous paper weight.

Project/Quality manager, Project management life cycle, Quality

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Kranthi Kumar R, Kiran Kumar Reddi, "A Project/Quality Manager’s Responsibility is Not Turning “Quality” To Be A Very Expensive Paper Weight," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.902-907, 2018.

An important measurement for the cost-effective growth of any nation is rapidly increasing vehicle count. The effect of raise in vehicle count grows the traffic congestion. It results in wastage of energy, time and environmental pollution. To meet the demands of an overgrowing city the traditional traffic lights deployed in cities are not sufficient since these traffic lights have specific predetermined time intervals for changing from a red phase to green phase. This major issue, that most of the cities is facing in spite of measures being taken to palliate and reduce it. In recent years traffic congestion has become apparent as one of the major challenges for engineers, planners, and policymakers, not in all urban setting, but worldwide. In this regard with the help of Intelligent Transportation Systems (ITS), several attempts were made to automate the traffic lights based on the density of vehicles on the road. Some researchers suggested the use of various distinctive sorts of strategies and computerized sensor frameworks to examine traffic density and to tackle the congestion issue depending on the traffic nature. This paper reviews different sensor frameworks by analyzing the pros and cons of each in cost, reliability, accuracy, efficiency, and maintenance overhead.

Intelligent Transportation Systems; Computer Vision; Machine Learning; Wireless Sensors; Traffic Control

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Pallavi A. Mandhare, Vilas Kharat, C.Y. Patil, "Intelligent Road Traffic Control System for Traffic Congestion: A Perspective," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.908-915, 2018.

There are several methods available; analytical (Exact), approximate and numerical; for solving differential equations. Most of these methods are computationally exhaustive because they require a lot of time and space. The Zhou’s differential transform technique has an edge over the traditional methods as it uses the polynomial as the approximation to exact solution. In this paper differential transform technique is employed to solve some nonlinear nonhomogeneous, initial value problems in system of differential equations which are often encountered in applied sciences and engineering. The solutions produced by differential transform method are compared with the exact solutions achieved by Laplace transform technique. It is observed that numerical results obtained by differential transform method are in good agreements with the analytical solutions.

Differential transform technique, System of differential equations, Laplace transforms technique, Exact solution

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S. P. Dahake, G. Purohit, A.V. Dubewar, "The Numerical Solution of Nonlinear Nonhomogeneous System of Differential Equations By Differential Transform Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.916-919, 2018.

Web usage Mining is an extension of traditional data mining. As the tremendous amount of data is increasing, the prominence of internet is growing. This impact upholds the user’s needs and the users are also increasing in enormous speed. Because of these realities the web data has been budding day to day. Therefore extracting the useful data from WWW has become the challenging one. Due to this fact the users are feeling disoriented. So it is necessary for the web usage miners to discover the new way of finding the desired information or the ease of accessing the web. As a result the web mining has become more popular and reached the peak in research field having in mind about mining the data and WWW as well. The aim of the proposed research is to survey on different Data preprocessing techniques carried out by most of researchers has been discussed, where this web log preparation is considered as the first step on web mining process to identify the user behavior. This phase is referred to be the most important process to ensure the quality of the log data. The log files are gathered and pre-processed by removing the unwanted or irrelevant information. A complete overview on data preprocessing may recommend better technique to find the user behavior and to improve the performance, and finally we concluded by providing a glimpse of various Web mining Applications.

Web usage mining, Web server log, Data Preprocessing, User identification, Session Identification

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R.Sandrilla, M. Savitha Devi, "A Study on Data Preprocessing Methods on Web Log Data in Web Usage Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.920-928, 2018.