Volume-07 , Special Issue-14 , May - 2019 Go Back

Healthcare has huge impact on the society and also holds more importance in which analytics are applied to achieve accurate results about patients and to identify bottlenecks and to increase the business efficiency. Hospital readmissions are way too expensive and reflect the insufficiency in the healthcare system. Since readmission into hospitals has become unaffordable necessary measure needs to be taken to make them preventable [1] Readmissions rate decides the quality of treatment provided by the hospitals. Mostly readmissions are caused due to improper medication, early discharge, unmonitored discharge and poor care of hospital staff. In USA alone treatment of readmitted diabetics patients has exceeded over 250 million dollars per year. Advance identification of patient having high risk of readmission can allow the healthcare providers to perform additional investigations and also provides possibility to prevent readmissions. This method improves the quality of care and also reduces the medical expenses caused due to readmission .Number of patient visits, discharge order, type of admission were identified as the predicators of readmission. It was found that based on number of laboratory tests and discharge order both together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. <30 days) or after a longer period of time (i.e. >30 days).These accurate results help the healthcare providers to improve care taken for diabetic patients.

Machine Learning, Analysis on Medical data, Data collection, Data preprocessing, Data labeling, Predictive modeling, Model training, Prediction

[1] Donzé J. Aujesky D., Williams D., Schnipper J.L, MD. ―Potentially avoidable 30-day hospital readmissions in medical patients: Derivation and validation of a prediction model. JAMA Internal Medicine,‖173(8):632-638, Apr. 2013.
[2] Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications,‖ Report of a WHO Consultation Part 1: Diagnosis and Classification of Diabetes Mellitus World Health Organization Department of Non communicable Disease Surveillance, Geneva, 1999.
[3] T. D. Briefing. Ahrq: The conditions that cause the most readmissions. The Daily Briefing. Web,2014..
[4] P.Yasodha and M. Kannan, "Analysis of a Population of Diabetic Patients Databases in WekaTool", International Journal of Scientific & Engineering Research.
[5] [2]A. Iyer, J. S and R. Sumbaly, "Diagnosis of Diabetes Using Classification Mining Techniques".
[6] UCI Machine Learning Repository http://www.ics.uci.edu/~mlearn/MLRepository.html
[7] T. D. Briefing. Ahrq: The conditions that cause the most readmissions. The Daily Briefing. Web, 2014.
[8] K. M. Dungan. The effect of diabetes on hospital readmissions. Journal of diabetes science and technology, 6(5):1045–1052, 2012.
[9] E. Eby, C. Hardwick, M. Yu, S. Gelwicks, K. Deschamps, J. Xie, and S. George. Predictors of 30 day hospital readmission in patients with type 2 diabetes: a retrospective, case-control, database study. Current Medical Research & Opinion, 31(1):107–114, 2014.
[10] H. Zhang. The optimality of naive bayes. AA, 1(2):3, 2004.

G. Amrutha Varshini, Nafisa.S, Priyanka, S. Sri Vishnupriya, Ambika B.J, "BigData Analytics Predicting Risk of Readmissions of Diabetic Patients", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.99-102, 2019.

The objective of this proposed work is to monitor the waste management system. The new era of web and Internet of things paradigm is being enabled by the proliferation of various devices like sensors, GSM modules and LCD displays. A smart device is embedded in the environment to monitor and collect all types of information. In this project, we specifically focus on the adaptation of smart device as a key enabling technology in contemporary waste management. We use sensors like MQ2, MQ3, fire, Load and IR sensor to detect smoke, harmful smell, fire, weight and the level of garbage in the large dustbin (one big dustbin for a community in that area or one for a building) which collect garbage from that locality or building and send a message through GSM module.

SST MC, IR sensor, MQ2, MQ3, Fire sensor, Load sensor, LCD display, GSM module

[1]. Monika K A, Nikitha Rao, Prapulla S B, Shobha G “Smart Dustbin-An Efficient Garbage Monitoring System” IJESC ISSN 2321 3361 Volume 6 Issue No. 6
[2].Nimmi Pandey, Shubhashree Bal, Gajal Bharti, Amit Sharma “Garbage Monitoring and Management using Sensors, RF- ID and GSM” International Journal of Innovative and Emerging Research in Engineering p-ISSN: 2394 5494Volume 2, Issue 3, 2015
[3]. Kanchan Mahajan, Prof. J.S.Chitode “Waste Bin Monitoring System Using Integrated Technologies” International Journal of Innovative Research in Science, Engineering and Technology ISSN: 2319 8753 Vol. 3, Issue 7, July 2014
[4]. Ann Mary Thomas, Annu Reji Philip, Tessy Elsa Peter, Er. Nishanth P R “Dust Bin Monitoring System” International Journal of Advanced Research in Computer and Communication Engineering ISSN (Online) 2278-1021 ISSN (Print) 2319 5940Vol. 5, Issue 3, March 2016
[5]. S.S.Navghane, M.S.Killedar, Dr.V.M.Rohokale “IoT Based Smart Garbage and Waste Collection Bin” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) ISSN: 2278 – 909X Volume 5, Issue 5, May 2016
[6]. Sandeep M. Chaware, Shriram Dighe, Akshay Joshi, Namrata Bajare, Rohini Korke “Smart Garbage Monitoring System using Internet of Things (IOT)” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering ISSN (Online) 2321 – 2004 ISSN (Print) 2321 – 5526 Vol. 5, Issue 1, January 2017
[7]. https://github.com/jakubroztocil/geotagger

Manjunath P C, Piojeet Sharma, Pratik Singh, Prashant Rai, "Smart Monitoring System for Swachh Bharat", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.103-106, 2019.

This observational research about how the web is presently falling flat laypeople who are scanning on the web for lawful help to their life issue and what a future plan of client focused guidelines and practices for better legitimate help on the Internet could be. It initially looks at the current writing about how the web can best be utilized as lawful asset and business as usual of legitimate help destinations. At that point it overviews and inspects negative buyer reports and surveys of legitimate help sites. At last, it introduces the investigation of how laypeople scan for assets to determine a legitimate issue, how they scout and asses lawful help administrations on the web, and their input on which existing lawful help locales they consider to be the most usable, the most dependable, and the most profitable. This information is valuable to propose new prescribed procedures about how these tech-based administrations can best serve laypeople, as far as convenience, nature of administration, and insurance of the clients` advantages. It likewise affirms the significance of the Internet as a lawful help administration and features the requirement for more innovative work on better online lawful help locales that fit laypeople`s needs and inclinations.

Case Handlers, Case Suppliers, Representatives

[1]. CLEO (Center for research and innovation, Public legal education and information in Ontario) communities; format and delivery channels 22 (2013).
[2]. American bar assocation standing committee on the delivery of legal services, Perspectives on finding personal legal services: Results of a public opinion poll (2011).
[3]. Thomas M. Clarke, Building a Litigant portal: Business and technical
[4]. requirements national center for state courts and state justice institute (2015).
[5]. Queensland association Of independent legal services INC., Queensland Community legalcenter’s use of technology literaturere view and discussion paper 18 (2014).

Ambika S, V. Tejasree, A. Sai Praneeth, V. Ashish Kumar, Asha K, "Design and Development of Legal Freelance Portal", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.107-111, 2019.

Nowadays, many expenses are made in the field of defense in adopting primitive security measures to protect the border from the trespassers. Some military organizations take the help of robot in the risk prone areas which are not that effective when executed by army men. These Arm robots are confining with the camera, sensors, metal detector and video recording, and the main objective of our system is to get camouflaged including some additional parameters like ZigBee wireless module for real time data processed by the camera at the video, audio and PIR sensor to trace the intruders. Here, we are coming up with new technology to overcome connectivity range when the arm robots are out of range through ZigBee modules.

camouflaged, connectivity, security, detection, identification, ZigBee-wireless

[1] YadnikaWarang, TejaliMahadik, Supriya Ojha, Asha Rawat,“Camouflage Robot-A Colour Changing Spy Robot”, International Journal of Advance Research and Innovative Ideas in Education, Vol-3 Issue-2 2017.
[2]. VivekKhot, Ravindra Joshi, Aashay Chavan, SanketDhumal ,“Camouflaged Colour Changing Robot for Military Purpose”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 3, March 2015.
[3]. Xiang Zhang, Ce Zhu, “Camouflage modeling for moving object detection”, IEEE, 03 September 2015.
[4]. Vishesh Goel, Tarun Jain, Sahil Singhal, Silica Kole, “Specific Colour Detection in Images using RGB Modelling in MATLAB”,International Journal of Computer Applications,Vol.161-No 8,March 2017.
[5]. Dr. Shantanu K. Dixit, “Design and Implementation of e-surveillance Robot for Video Monitoring and Living Body Detection”, International Journal of Scientific and Research Publication, Volume 4, issue 4, April 2014, ISSN 2250- 3153.
[6]. Dhiraj Singh Patel, “Mobile Operated Spy Robot”: International Journal of Emerging Technology and Advance Engineering, Volume 3, special issue 2, Jan 2013.
[7]. Dr. S. Bhargavi “Design of an Intelligent Combat Robot for War Field”, International Journal of Advance Compute Science and Application, Volume 2, no.8, 2011.

Dinesh G P, Mohammed Haneef, Mohammed Junaid, Naveen Kumar, Kanaiya VK, "Camouflage Surveillance Robot", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.112-115, 2019.

The process of identifying and segmenting nuclei in the cell is a prerequisite for the analysis of various genetic disorders. The main carrier of genetic information in most of the living organisms is Deoxyribonucleic acid(DNA) which is present in the nucleus of the cell. Detection and segmentation of nuclei is laborious and time demanding. This paper intends to explore an untouched approach towards solving the issue by automating the process which drastically reduces the development time and required man power. Many classic methods like Otsu, watershed were proposed but they failed to accurately segment and few caused over segmentation. In the recent timespan, the executions of Convolutional Neural Networks (CNN) have made it evident that they demonstrate impressive performance on biomedical image classification. CNN methods also face issues with stipulation for hefty delineated tutoring data sets but in this context, a CNN architecture U-Net which is proficient of grasping knowledge from smaller pre-processed augmented data-set is proposed. The proposed encoder-decoder U-Net model indicates better execution in identifying genuine fragments contrasted with the cutting edge system for rapid CNN shows better performance in detecting true segments compared to the state-of-the-art technique Faster Recurrent-CNN (R-CNN).

CNN, R-CNN, U-Net Model

[1] O.Ronneberger, P.Fischer,T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, arXiv:1505.04597v1 [cs.CV] 18 May 2015
[2] H.Irshad, A.Veillard, L.Roux, and Daniel Racoceanu, “Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential ” IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 7, 2014
[3] ZENG,W.XIE,Y.ZHANG,YAO LU “RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images” 10.1109/ACCESS.2019.2896920, IEEE
[4] J. Kong, L. Cooper, T. Kurc, D. Brat, and J. Saltz, “Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma,” in Proc. IEEE 33rd Annu. Int. Conf. Eng. Med. Biol. Soc., Boston, MA, USA, Aug. 30–Sep. 3, 2011, pp. 6605–6608.
[5] V.-T. Ta, O. Lezoray, A. Elmoataz, and S. Sch ´ upp, “Graph-based tools ¨ for microscopic cellular image segmentation,” Pattern Recog., vol. 42, no. 6, pp. 1113–1125, 2009.
[6] Md Alom, Chris Yakopcic, M. Taha , V.K.Asari “Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)” IEEE 2018
[7] A. Hafiane, F. Bunyak, and K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation,” in Proc. 19th Int. Conf. Pattern Recog., Tampa, FL, USA, Dec. 2008
[8] C. Wahlby, I. M. Sintorn, F. Erlandsson, G. Borgefors, and E. Bengtsson, “Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections,” J. Microsc., vol. 215, no. 1, pp. 67–76, 2004
[9] J. Vink, M. V. Leeuwen, C. V. Deurzen, and G. Haan, “Efficient nucleus detector in histopathology images,” J. Microsc., vol. 249, no. 2, pp. 124– 135, 2013
[10] Yu He,Xi Yu,Chang Liu,Jian Zhang,Ke Hu,H C Zhu “A 3D Dual Path U-Net of Cancer Segmentation Based on MRI” 3rd IEEE International Conference on Image, Vision and Computing 2018.
[11] S. Wienert, D. Heim, K. Saeger, A. Stenzinger, M. Beil, P. Hufnagl, M. Dietel, C. Denkert, and F. Klauschen, “Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach,” Sci. Rep., vol. 2, 7 pp., 2012.
[12] H. Chang, L. A. Loss, and B. Parvin, “Nuclear segmentation in H&E sections via multi-reference graph cut (MRGC),” in Proc. 9th IEEE Int. Symp. Biomed. Imag.: Nano Macro, Barcelona, Spain, pp. 614– 617, 2012
[13] S. M.K Hasan and Cristian A. Linte,Chester F. Carlson “A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation” IEEE 2018
[14] C. Jung and C. Kim, “Segmenting clustered nuclei using h-minima transform-based marker extraction and contour parameterization,” IEEE Trans. Biomed. Eng., vol. 57, no. 10, pp. 2600–2604, Oct. 2010.
[15] C. Jung, C. Kim, S. W. Chae, and S. Oh, “Unsupervised segmentation of overlapped nuclei using Bayesian classification,” IEEE Trans. Biomed. Eng., vol. 57, no. 12, pp. 2825–2832, Dec. 2010.
[16] Martin Kolaˇr´ık, Radim Burget, Vaclav Uher, Malay Kishore Dutta “3D Dense-U-Net for MRI Brain Tissue Segmentation”IEEE 2018
[17] S. Ali and A. Madabhushi, “An integrated region-, boundary-, shapebased active contour for multiple object overlap resolution in histological imagery,” IEEE Trans. Med. Imag., vol. 31, no. 7, pp. 1448–1460, Jul. 2012
[18] Ling Luo,Dali Chen,Dingyu Xue1 “Retinal Blood Vessels Semantic Segmentation Method Based on Modified U-Net” IEEE 2018
[19] M. Dundar, S. Badve, G. Bilgin, V. C. Raykar, R. K. Jain, O. Sertel, and M. N. Gurcan, “Computerized classification of intraductal breast lesions using histopathological images,” IEEE Trans. Biomed. Eng., vol. 58, no. 7, pp. 1977–1984, Jul. 2011.
[20] L. Yang, O. Tuzel, P. Meer, and D. Foran, “Automatic image analysis of histopathology specimens using concave vertex graph,” in Proc. Med. Image Comput. Comput.-Assist. Interv., New York, NY, USA, 2008
[21] Reza Moradi Rad, Parvaneh Saeedi, Jason Au, and Jon Havelock “MULTI-RESOLUTIONAL ENSEMBLE OF STACKED DILATED U-NET FOR INNER CELL MASS SEGMENTATION IN HUMAN EMBRYONIC IMAGES” IEEE 2018
[22] X. Qi, F. Xing, D. J. Foran, and L. Yang, “Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set,” IEEE Trans. Biomed. Eng., vol. 59, no. 3, pp. 754–765, Mar. 2012.
[23] Humera Shaziya, K. Shyamala and Raniah Zaheer “Automatic Lung Segmentation on Thoracic CT Scans using U-Net Convolutional Network” International Conference on Communication and Signal Processing, IEEE 2018
[24] P. W. Huang and Y. H. Lai, “Effective segmentation and classification for HCC biopsy images,” Pattern Recog., vol. 43, no. 4, pp. 1550–1563, 2010.
[25] Dong Yang,Qiaoying Huang, Leon Axel, Dimitris Metaxas1 “MULTI-COMPONENT DEFORMABLE MODELS COUPLED WITH 2D-3D U-NET FOR AUTOMATED PROBABILISTIC SEGMENTATION OF CARDIAC WALLS AND BLOOD” IEEE 15th International Symposium on Biomedical Imaging 2018
[26] Lu Zhang,Li Xu “An Automatic Liver Segmentation Algorithm for CT Images U-net with separated paths of feature extraction” 3rd IEEE International Conference on Image, Vision and Computing 2018

Besiahgari Dinesh, B.Kavya, Besiahgari Sree Avinash, Sarvamangala D R, "Mechanisation of Nuclei detection and segmentation:a leap in Medical Research", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.116-120, 2019.

Cloud storage has been widely used because it can provide seemingly unlimited storage space and flexible access, while the storage resource is vulnerable to the cost issue since the data should be maintained for a long time. Data deduplication techniques make sure that only one distinctive instance of knowledge is maintained on storage media.In this paper, we discuss the benefit when a deduplication technique is adopted to the cloud storage, then we propose a deduplication framework for cloud environments. The deduplication application divides a given file into smaller chunks, then searches the index table that consists of hash values of chunks to judge duplicate data, finally stores non repeated chunks.

Deduplication, MD5, Cloud, Hash

[1] Akanksha Upadhyay, Abha Sharma, DIFFERENT SECURE DATA DEDUPLICATION APPROACHES FOR CLOUD STORAGE: A REVIEW. IJARCS Volume 9, No. 3, May-June 2018
[2] Sarah Prithvika P.C , Ramani S , Jakkulin Joshi J and Sindhu K, Data Deduplication in Cloud Environment – A Survey. www.ijlemr.com || Volume 03 - Issue 01 || January 2018 || PP. 44-4
[3] Sumedh Deshpande, Anupama Murkute, Saurabh Patil, Sachin Kamble, Prof. Swati Shekapure, Deduplication on Encrypted Big data in Cloud. IJIRSET Vol. 7, Issue 4, April 2018 Vol. 7, Issue 4, April 2018
[4] Priyanka G. Masal, B.M. Patil , Encrypted Big Data with Data Deduplication in Cloud. International Journal of Computer Applications (0975 – 8887) Volume 174 – No.6, September 2017
[5] Bhairavi Kesalkar , Dipali Bagade , Manjusha Barsagade , Namita Jakulwar, Prof. Shrikant Zade , IMPLEMENTATION OF DATA DEDUPLICATION USING CLOUD COMPUTING. IJARIIT( ISSN: 2454-132X) KITE/NCISRDC/IJARIIT/2018/CSE/105
[6] Sabale Nikita C, Prof.N.G.Pardeshi, A Survey Paper on Deduplication on Encrypted Big Data Using HDFS Framework. IJIRCCE Vol. 5, Issue 6, June 2017
[7] Priyadharsini.P, Dhamodran.P, Kavitha.M.S, A SURVEY ON DE-DUPLICATION IN CLOUD COMPUTING. IJCSMC, Vol. 3, Issue. 11, November 2014, pg.149 – 155
[8] Kinzal Patel, Prof. Kapildev Naina, Review on Data Deduplication In Cloud Computing. IJAERD Volume 4, Issue 11, November -2017
[9] Vaishnavi Moorthy, Arpit Parwal and Udit Rout, DE-DUPLICATION IN CLOUD STORAGE USING HASHING TECHNIQUE FOR ENCRYPTED DATA. ARPN VOL. 13, NO. 5, MARCH 2018
[10] Sadhana Poornachandra Rao, M.Kusuma, Application-Aware Big Data Deduplication in Cloud Environment. April 2018 | IJIRT | Volume 4 Issue 11 | ISSN: 2349-6002
[11] Dastagir Shaikh, Pratik Sen , Zubair Inamdar, Avoidance of Duplication of Encrypted Big-data in Cloud Storage IJARCCE Vol. 6, Issue 4, April 2017
[12] Supriya Milind More, Kailas Devadkar, A Comparative Survey on Big Data Deduplication Techniques for Efficient Storage System. IJIACS ISSN 2347 – 8616 Volume 7, Issue 3 March 2018.
[13] Zheng Yan, Deduplicatrd on encrypted big data in cloud. Volume 02 no 02 april-june 2016

Jagadeeshwari J, Joshitha Gunreddy, Harshitha H G, Harshitha R, Spoorthi Rakesh, "Application-aware big-data de-duplication in cloud computing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.121-124, 2019.

In this paper, we offer the entire description of the existing system followed by solutions in greenhouse monitoring. Then we have conducted a survey on the related literature and concisely explaining them to the reviewer about the proposed work. The main aim of this survey is to make a way for the readers to recognize the developments and issues in this area. This paper concentrates to provide information to the farmers to develop agriculture so that they could grow crops according to their farm conditions allowing in enhancing productivity and sales. The approach in carrying out the survey is to know about various technologies that have been implemented in monitoring the greenhouse and also to know the enhancements that could be made further. The survey has been made to analyze to what extent the farmers have been benefited or not by reviewing the various papers and to make an attempt to understand the levels of difficulties in monitoring greenhouse with exact temperature, humidity, soil moisture, water level and, aid farmers to perceive profit and also to resolve the same.

Microcontroller, GSM, Temperature Sensor, Humidity Sensor, Soil Sensor, Zigbee Technology, Wireless Sensor network. greenhouse monitoring

[1] Pradorn Sureephong , Patcharapong Wiangnak & Santichai Wicha "The comparison of soil sensors for integrated creation of IOT based Wetting front detector (WFD) with an efficient irrigation system to support precision farming" 978-1-5090-5210-3/17/$31.00©2017 IEEE
[2] Dr.D.K. Sreekantha, Kavya.A.M, "Agricultural crop monitoring using IOT A study", 2017 11th International Conference on Intelligent Systems and Control (ISCO) 978-1-5090-2717-0/17/$31.00 ©2017 IEEE
[3] R.suresh, S.Gopinath, K.Govindaraju, T.Devika4, N.SuthanthiraVanitha "GSM based Automated Irrigation Control using Raingun Irrigation System", International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 2, February 2014
[4] H.T.Ingale, N.N.Kasat 1GF`s G.C.O.E, Jalgaon, SIPNA`s C.O.E.T, "Automated Irrigation System", International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 4, Issue 11 (November 2014), PP. 51-54.
[5] K.S.S. Prasad, Nitesh Kumar, Nitish Kumar Sinha and Palash Kumar Saha "Water-Saving Irrigation System Based on Automatic Control by Using GSM Technology", Middle-East Journal of Scientific Research 12 (12): 1824-1827, 20142 ISSN 1990-9233 © IDOSI Publications, 2014 DOI: 10.5829/idosi.mejsr.2012.12.12.1258.
[6] Chandrika Chanda, Surbhi Agarwal, Er. B.Persis Urbana Ivy, AP(SG) "A Survey of Automated GSM Based irrigation systems" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 10, October 2013 ).
[7] Shiraz Pasha B.R, Dr. B Yogesha "Microcontroller Based Automated Irrigation System" The International Journal Of Engineering And Science (IJES) || Volume || 3 || Issue || 7 || Pages || 06-9 || 2014 || ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
[8] Mahesh M. Galgalikar, Gayatri S Deshmukh "Real-Time Automization of Irrigation system for Social Modernization of Indian Agricultural System", ©2013International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 22.
[9] Nikkila, R., Seilonen, I., Koskinen, K. 2010. ‘‘Software Architecture for Farm Management Information Systems in Precision Agriculture.`` , Comput. Electron. Agric. 70 (2), 328-336.
[10] Xijun Xing, Jiancheng Song, Lingyan Lin, Muqin Tian, Zhipeng Lei “Development of Intelligent Information Monitoring System in Greenhouse Based on Wireless Sensor Network”2017 4th International Conference on Information Science and Control Engineering.
[11] Lijun Liu, Wei Jiang “Design of Vegetable Greenhouse Monitoring System Based on ZigBee and GPRS” 2018 4th International Conference on Control, Automation and Robotics.
[12] LI Xiaofeng, QIN Linlin, LU Linjian, WU Gang “Design and Implementation of Modern Greenhouse Remote Monitoring System Based on the Android System”Proceedings of the 34th Chinese Control Conference July 28-30, 2015, Hangzhou, China.
[13] M. Danita, Blessy Mathew, Nithila Shereen, Namrata Sharon, J. John Paul “IoT based Automated Greenhouse Monitoring System”Proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS 2018) IEEE Xplore Compliant Part Number: CFP18K74-ART; ISBN:978-1-5386-2842-3.
[14] Lijun Liu, Yang Zhang “Design of Greenhouse Environment Monitoring System Based on Wireless Sensor Network” 2017 3rd International Conference on Control, Automation and Robotics 978-1-5090-6088-7117/$31.00 ©20 17 IEEE.
[15] Niamul Hassan, Shihab Ibne Abdullah, Ahmad Shams Noor, Marzia Alam “An Automatic Monitoring and Control System Inside Greenhouse” 978-1-5090-0169-9/15/$31.00 ©2015 IEEE.

Dhanya N A, Kiran Kumari Patil, "Monitoring of Greenhouse Powered By Machine Learning & Internet of Things: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.125-130, 2019.

Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy prediction system to categorize the companies based on extent of risk. The prediction system acts as a decision support tool for detection of bankruptcy

Bankruptcy, soft computing, decision support tool

[1] Hauser, R.P. and Booth, D., “Predicting Bankruptcy with Robust Logistic Regression”, Journal of Data Science , Vol 9, pp. 565-584, 2011.
[2] Kim, M.-J. and Han, I. , ”The Discovery of Experts’ Decision Ruels from Qualitative Bankruptcy Data Using Genetic Algorithms”, Expert Systems with Application , Vol 25, pp. 637-646, 2003.
[3] Pedregosa, et al., “Scikit-Learn: Machine Learning in Python”, Journal of M achine Learning Research , Vol 12, pp. 2825-2830, 2011.
[4] Sirvastava, N., et al., “Dropout: A Simple Way to Prevent Neural Networks from Overfitting, “Journal of Machine Learning Research, Vol 15, 1929-1958, 2011.
[5] Dev, D., “Deep Learning with Hadoop”, Packet Publishing, Birmingham, 52, 2017.
[6] Nielsen, F., “Neural Networks—Algorithms and Applications”, https://www.mendeley.com/research-papers/neural-networks-algorithms-applicatio ns-5/
[7] Robinson, N., “The Disadvantages of Logistic Regression. http://classroom.synonym.com/disadvantages-logistic-regression-8574447.html “
[8] Sima, J. (1998) Introduction to Neural Networks. Technical Report No. 755.
[9] Baldi, P. (2012) Autoencoders, Unsupervised Learning, and Deep Architectures. Journal of Machine Learning Research , 27, 37-50.
[10] Martin, A., Uthayakumar, J. and Nadarajan, M. (2014) Qualitative Bankruptcy Data Set, UCI.
[11] Ramosacaj, Miftar & Hasani, Vjollca & Dumi, Alba. (2015). Application of Logistic Regression in the Study of Students’ Performance Level (Case Study of Vlora University). Journal of Educational and Social Research.
[12] Abedin, Tasnima & Chowdhury, Mohammad & Afzal, Arfan & F, Yeasmin & Turin, Tanvir. (2016). Application of Binary Logistic Regression in Clinical Research. Journal of National Heart Foundation of Bangladesh.

Sagar Kumar, Shubhajit Mukherjee, Shubham Agrawal, Ila Chandrakar, "Logistic Regression for Detection of Bankruptcy", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.131-133, 2019.

The target of this venture is to screen air pollution on the streets and track a vehicle which causes air pollution. So as to accomplish this, numerous nations in the world have displayed a progression of outflows measures, in the interim a few strategies have been created, including update engine motor or improving the nature of the fuel. But these activities have not realized a striking impact in controlling the contamination. In this framework, Radio Frequency Identification (RFID) technology is used and to implement this technology, the remote specialized technique is embraced to gather and transmit emission data of vehicles. The utilization of the Internet of Things (IoT) to keep track of vehicles that pollute is proposed. In addition, the RFID gadgets should be introduced on the traffic lights with the goal that emissions signals from a vehicle can be cross-examined when the vehicles stop in the junctions. By applying the framework, the amount of air pollution that is being caused by vehicles can be reduced.

Internet of Things; Radio Frequency Identification

[1] Frank A, Bender, Martin Kaszynski, and Oliver Sawodny (2013), ”Drive cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles”, IEEE TransactionsOnVehicular Technology, VOL. 62, PP 8.
[2] Amit.V.Kachavimath (2015), ”Control of Vehicle Effluence through Internet of Things & Android”, IJCST, Volume 3, Issue 5, PP 2347-8578.
[3] Chi-Man Vong, Pak-Kin Wong, Weng-Fai Ip (2012), “Framework of Vehicle Emission Inspection an Control through RFID and Traffic Lights”, PP 978-1-61284-471.
[4] T. Leelaram et al. (2015), “RFID Based Vehicle Emission in Cities on the Internet of Thing”, IJRMEET, Vol 3, Issue 2, PP 2320-6586.
[5] Hamed Noori (2013), “Modelling the Impact of VANET Enabled Traffic Lights Control on the Response Time of Emergency Vehicles in Realistic Large-Scale Urban Area”, IEEE International conference on communication, PP 978 -1-4673.
[6] Pei-Chi Hsieh, You-Ren Chen, Wen-Hao Wu, and Pao-Ann Hsiung (2013), “Timing Optimization and Control for Smart Traffic”, IEEE International Conference on Internet of Things, Volume 3, PP 40-45.
[7] Minghe Yu, Dapeng Zhang, Yurong Cheng, and Mingshun Wang (2012), “An RFID Electronic Tag based Automatic Vehicle Identification System for Traffic IOT Applications”, IEEE, PP 978-1-4244-8738-7.
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Sathish G C, P. Viswa Teja, Pallavi R, Pavani, P. Vengal, "Emission Detection Using RFID Technology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.134-136, 2019.

Bullying has many forms like invalid criticism, intimidation, false allegations, bantering, humiliation or unnecessary written warnings. In this age of connectivity cyber bullying exists at workspace and even in schools or colleges. We have a simple yet effective solution, which we provide by the means of our platform. Administration or a cautionary oversight is all that’s required at times to prevent an individual from going down the wrong path and in this belief that we provide the concerned parents or some representative peer an automated management system to watch over the day to day communicate by providing remote access. This is a survey of facts and figures, using which we will implement data analysis and analytics techniques to effectively extract useful insights. These insights help us assimilate the depths of the problem domain. Using this information, we will effectively counter cyberbullying in all its various forms.

Cyberbullying, Automated System, Data Analysis, Data Analytics

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Akash Balachandar, Anusha D Kulkarni, Ashwini N Shetty, Kiran Kumari Patil , "Cyber Patrol – A Cyber Bullying Solution", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.137-140, 2019.