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To generate revenue for any service-based industry, selling the right product to the right customer at a right time is the key aspect. Airline industry is an example of such an industry which could get benefit from knowing the right type of customers. This type of customers can be found out by analyzing behavioral patterns over a brief period of time. . Cancellation of flight ticket bookings is an interesting aspect from the perspective of Airline industries. If there is a system available which can predict about customer’s cancellation of booking then it can be exploited for huge profits and identifying customers which might possibly cancel their bookings is one of the many tasks that can be achieved by leveraging Data Analytics and Machine Learning techniques. Our goal is to design and implement a Classification model which will predict cancellation of ticket booked. We intend to achieve this goal by analyzing ticket booking data of a domestic Indian airline with the help of data analysis techniques to find some interesting patterns in the data. The predicted output will help to scale down the loss of Airline industries.

Cancellation prediction, Flight data analytics, Machine learning

[1] W. McKinney, pandas: a python data analysis library, http://pandas.sourceforge.net [scipy2010].
[2] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot and Edouard Duchesna: Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12 (2011).
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Ahlam Ansari, Salim Mapkar, Ashad Shaikh, Maaz Khan, "Cancellation Prediction for Flight Bookings using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.319-321, 2019.

At this state of affairs several issues are faced by farmers in India, we have discovered that there is rise in suicide rate over the years. The reasons behind this includes weather conditions, debt, family issues and frequent change in Indian government norms. Sometimes farmers are not aware about the crop which suits their soil quality, soil nutrients and soil composition. Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources.

Climate, Sensors ,machine learning, agricultural productivity, crop production, prediction

[1] Nishit Jain, Amit Kumar, SahilGarud, Vishal Pradhan, PrajaktaKulkarni, “Crop Selection Method Based on Various Environmental Factors Using Machine Learning”,International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 02, Feb -2017.
[2] Igor Oliveira, Renato L. F. Cunha, Bruno Silva, Marco A. S. Netto, “A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast”,25 June 2018,14th IEEE eScience,https://arxiv.org/abs/1806.09244
[3] Rushika Ghadge, Juilee Kulkarni, Pooja More, Sachee Nene, Priya R L, “Prediction of crop yield using machine learning”, 2018 - International Research Journal of Engineering and Technology.
[4] S.Veenadhari, Dr. Bharat Misra, Dr.CD Singh, “Machine learning approach for forecasting crop yield based on climatic parameters”, International Conference on Computer Communication and Informatics (ICCCI -2014),Conference paper, Jan. 03 -05, 2014.
[5] Niketa Gandhi, LeisaJ.Armstrong, OwaizPetkar, Amiya Kumar Tripathi, “Rice crop yield prediction in India using SVM (Support Vector Machine).”, 2016 - 13th International Joint Conference on Computer Science and Software Engineering.
[6] Md. Tahmid Shakoor, Karishma Rahman, Sumaiya NasrinRayta, AmitabhaChakrabarty, “Agricultural Production Output Prediction Using Supervised Machine Learning Techniques”, IEEE, 2017.
[7] Prof.K.D.Yesugade, Sonal Bathiya, Priya Bora,Nikita Waykule,”Agro-sense:A Mobile App For Efficient Farming System Using Sensors”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 04,,April -2015.
[8] Rabina Dayal, Arun Kumar Yadav, “A Review of Different Techniques Utilized for Crop Yield Prediction”, International Journal Of Computer Science And Engineering, Vol.6, Issue.12, pp.437-442,2018

K.D. Yesugade, Hetanshi Chudasama, Aditi Kharde, Ketki Mirashi, Kajal Muley, "Crop Suggesting System Using Unsupervised Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.322-325, 2019.

In this paper, we generate paths and cycles using hyperedge replacement graph grammars and hyperedge replacement graph P systems. We observe that the generative power is increased when we use P system to generate paths and cycles. This paper is the impact of Jeltsch and Kreowski work on grammatical inference based on hyperedge replacement. For special classes of graphs namely paths and cycles an alternative method is given to infer the exact grammar using edge contraction between the adjacent vertices.

Graph Grammars, Hyperedge replacement, Grammatical Inference

[1] A.Habel , H.J. Kreowski ..:“May We introduce to you: Hyperedge
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26 ,1987.
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[4] G.Paun” A guide to membrane computing”, Theoretical Computer Science, Vol.287, 73-100, 2002,.
[5] Colin de la Higuera, “Current Trends in Grammatical Inference”,
Lecture Notes in Computer Science, 1876,28-31, 2000.
[6] G.Paun, Rozenberg.G.,Saloma .A., “The oxford Handbook of Membrane Computing”,2010
[7] Meena Parvathy Sankar, N.G.David ,D.G.Thomas ,”Hyperedge replacement Graph P system”, Proceeding BIC-TA’11, Proceedings of the 2011 Sixth International Conference on Bio-Inspired Computing: theories and Applications,2011.
[8] E.Jeltsch , H.J Kreowski.: “Grammatical Inference based on Hyperedge Replacement” ,Lecture Notes in Computer Science, vol 32, pp 461-474, 1990.

Thanga Murugeshwari. V, Emerald Princess Sheela J.D., "Generation of Paths and Cycles Using Hyperedge Replacement and Their Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.326-330, 2019.

In many countries like India, Child abuse is one of the major social evil leading to poor emotional health of the future citizens. Child abuse and neglect occur in different situations, for a different range of reasons at different places. Children often experience more than one form of abuse at a time. The abuse can take many forms such as physical, emotional, psychological, neglect, domestic violence etc. Recent research by McGill University (2015) showed that emotional abuse of a child may be as harmful as physical abuse and neglect. A child facing abuse in any of the above forms is at risk of being isolated; experience anxiety, depression, and mental trauma; face difficulties in learning and developing social relations. Although there have been many initiatives by NGOs and government and other welfare departments to stop this evil by registering a complaint against the abuser, conducting campaigns, by protesting against abuse and so on. These schemes are only carried out if the crime has already taken place. Thus the abuser can be punished according to the Child Protection and Safety Act but the negative impact it had left on the child remains. This can only be reduced if the responsible guardians or parents are alerted beforehand by an intelligent system, so that they can act in time to protect their child from this terrible faith.

Child abuse, Raspberry pi 3b+,Voice recognition API

[1] Mazlan Bin Che Soh, Nur Qistiena Nodin, Muhammad Hafiz Arshad, “Child abuse: A study on public perceptions about relationship between demographic factors and level of awareness towards child abuse”, IEEE Symposium Humanities, Science and Engineering Research (SHUSER),2012.
[2] S. Ansari, R. Siddique, R. Hamdulay, R. Quraishi and S. Samiya, "Real-Time Child Abuse and Reporting System," 2018 Fourth International Conference on Advances in Electrical, Electronics,Information, Communication and Bio-Informatics (AEEICB).
[3] Maksimovic, Mirjana & Vujovic, Vladimir & Davidović, Nikola & Milosevic, Vladimir & Perisic, Branko. (2014). Raspberry Pi as Internet of Things hardware: Performances and Constraints.
[4] Dhruv Chand M, Sreecharan Sankaranarayanan,Chandramouli Sharma “Project Jagriti: Crowdsourced child abuse reporting”, Global Humanitarian Technology Conference (GHTC), 2014
[5] C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen,Z. Chen, A. Kannan, R. J.Weiss, K. Rao, K. Gonina et al., “Stateof-the-art speech recognition with sequence-to-sequence models,” arXiv preprint arXiv:1712.01769, 2017.
[6] W. McKinney, pandas: a python data analysis library.
[7] Bird , S. (2006). NLTK: The natural language toolkit. In Proceedings of the COLING/ACL on interactive presentation sessions (pp. 69–72). Association for Computational Linguistics.

Ahlam Ansari, Salim Mapkar, Ashad Shaikh, Maaz Khan, "Child Abusing and Reporting System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.331-333, 2019.

In recent days, routing and security in the Mobile Ad hoc wireless Network is becoming more popular. The increased usage of mobile phones and internet connections facilitates the need of Mobile ad hoc wireless network. As the technology grows, the security and threats are also growing in an increased rate. Further, ensuring security for these networks is big challenge. Hence, this paper focuses on the area of Mobile ad hoc network and provides a comprehensive literature on the node stability, localization and routing. Further, this paper extensively review the routing strategies carried out in Sensor and Mobile Ad hoc Network. In addition, it also gives a deep insight about the challenges involved and precisely categorizes it with a complete study. Finally, this paper concludes by leveraging the lessons learnt from this survey.

MANET, MASNET, WSN, Cluster Head

[1]Tianshu Wang, Gongxuan Zhang, Xichen Yang and AhmadrezaVajdi, “Genetic algorithm for energy-efficent clustering and routing in wireless sensor networks”, The Journal of Systems and Software, 146, 196-214, 2018.
[2]J.T. Thirukrishna, S. Karthik and V.P. Arunachalam, “Revamp energy efficiency in Homogeneous Wireless Sensor Networks using Optimized Radio Energy Algorithm (OREA) and Power-Aware Distance Source Routing protocol”, Future Generation Computer Systems, 81, 331-339, 2018.
[3]Hiren Kumar Deva Sarma, Rajib Mall, and AvijitKar, “E2R2: Energy-Efficient and Reliable Routing for Mobile Wireless Sensor Networks”, IEEE Systems Journal, Vol. 10, No. 2, June 2016.
[4]Raghunandan G.H, Dr. A.Shobha Rani, Nanditha.S.Y and Swathi.G, “Hierarchical Agglomerative Clustering based Routing Algorithm for Overall Efficiency of Wireless Sensor Network”, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017.
[5]Hai Lin, Lusheng Wang, and Ruoshan Kong, “Energy Efficient Clustering Protocol forLarge-Scale Sensor Networks”, IEEE Sensors Journal, Vol. 15, No. 12, December 2015.
[6]Yan Gu, Dahai Jing and JieGuo, “Energy efficient Layered Clustering approach for WSN”, 2012 International Conference on Control Engineering and Communication Technology, 2012.
[7]TianshuWanga, Gongxuan Zhang, XichenYangc and AhmadrezaVajdi, “Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks”, The Journal of System and Software, Vol. 146, No. 196-214, 2018.
[8]Maya M. Warrier and Ajay Kumar, “An energy efficient approach for routing in wireless sensor networks”, Procedia Technology, Vol. 25, No. 520 – 527, 2016.
[9]Dimitrios Zorbas, Patrice Raveneau, YacineGhamri-Doudane and Christos Douligeris, “The charger positioning problem in clustered RF-power harvesting wireless sensor networks”, Ad Hoc Networks, Vol.78, 42–53, 2018.
[10]Sudhir Kumar, “Compartmental Modelling of Opportunistic Signals for Energy Efficient Optimal Clustering in WSN”, IEEE Communications Letters, Vol. 22, No. 1, January 2018.
[11]LI Han, “A Multiple-Hop Energy Efficient Clustered Algorithm for Heterogeneous WSN”, 2012 Fourth International Conference on Multimedia Information Networking and Security, 2012.
[12]Jianhua Huang, DanweiRuan and WeiqiangMeng, “An annulus sector grid aided energy-efficient multi-hop routing protocol for wireless sensor networks”, Computer Networks, Vol. 147. No. 38-48, 2018.
[13]Debashis De, Aditi Sen and Madhuparna Das Gupta, “Cluster Based Energy Efficient Lifetime Improvement Mechanism for WSN with Multiple Mobile Sink and Single Static Sink”, 2012 Third International Conference on Computer and Communication Technology, 2012.
[14]Wael Ali Hussein, Borhanuddin M Ali, MFA Rasid and FazirulhisyamHashim, “Design and Performance Analysis of High Reliability-optimal Routing protocol for Mobile Wireless Multimedia Sensor Networks”, 2017 IEEE 13th Malaysia International Conference on Communications (MICC), 28-30 Nov. 2017, The Puteri Pacific, Johor Bahru, Malaysia.
[15]Kaushik Gotefode and KishorKolhe, “Energy Efficiency in Wireless Sensor Network using Fuzzy rule and Tree Based Routing Protocol”, 2015 International Conference on Energy Systems and Applications (ICESA 2015).
[16]Sethuraman, Sibi Chakkaravarthy; Dhamodaran, Sangeetha; Vijayakumar, Vaidehi: `Intrusion detection system for detecting wireless attacks in IEEE 802.11 networks`, IET Networks, 2018, DOI: 10.1049/iet-net.2018.5050
[17]S Sibi Chakkaravarthy & V Vaidehi & P Rajesh, 2018. "Hybrid Analysis Technique to detect Advanced Persistent Threats," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 14(2), pages 59-76, April.

N.Ravi, G. Ramachandran, "A State of the Art Review on Mobile Ad hoc and Wireless Sensor," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.334-339, 2019.

Skin cancer is nothing but the increasing growth of abnormal skin cells. It occurs when unrepaired DNA damage to skin cells begins the mutations, or genetic defects, that lead the skin cells to multiply rapidly and form malignant tumors. Malignant melanoma is considered as one of the most dangerous skin cancers as it increases the mortality rate. Computer-aided diagnosis systems can helps to detect melanoma early. In the last decades, skin cancer increased and its incidence becoming a public health problem. Technological advances have allowed the development of applications that helps the early detection of melanoma. In this context, an Image Processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks and NB which are used perform a classification of the different kinds of moles.

Melanoma; Image Processing; Artificial Intelligence; Convolutional Neural Networks; Naïve Bayes

[1] Simes, M. C. F., J. J. S. Sousa, A. A. C. C. Pais. ”Skin cancer and new treatment perspectives : A review.” Cancer letters, Vol.357, Issue.1, pp.8-42, 2015.
[2] DS. Rigel, J. Russak, R. Friedman, “The evolution of melanoma diagnosis: 25 years beyond the ABCD,” CA: To Cancer J., no. 5, pp. 301- 316.
[3] Samy Bakheet, “An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features” Computation 2017, Vol.5, Issue.1, pp.1-4, 2017.
[4] Zhen Yu, “Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images”, Springer International Publishing Switzerland, pp. 238–246, 2017.
[5] C. Marín, GH. Alferez, J. Córdova, V. Gonzalez. “Detection of Melanoma Through Image Recognition and Artificial Neural Networks,” in Proc. World Congress on Medical Physics and Biomedical Engineering (TOILET’2015), Toronto, Canada. Jan. 2015.
[6] C. F. Ocampo, “Tool support the diagnosis of melanoma using images dermatoscopic or to Support Tool for Melanoma Diagnosis by using Dermoscopy Images,” Ph.D. dissertation, Univ. of Manizales Colombia, Colombia. 2011.
[7] Ferris, Laura K., et al. ”Computer-aided classification of melanocytic lesions using dermoscopic images.” Journal of the American Academy of Dermatology, Vol.73, Issue.5 , pp.769-776, 2015.
[8] Alcn, Jos Fernndez, et al. ”Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis.” IEEE journal of selected topics in signal processing , Vol.3, Issue.1, pp.14-25, 2009.
[9] Kasmi, Reda, and Karim Mokrani. ”Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule.” IET Image Processing , Vol.10, Issue.6, pp.448-455, 2016.
[10] Wilson F. Cueva, F. Muñoz, G. Vásquez., G. Delgado ,” Detection of skin cancer “Melanoma” through Computer Vision”, Published in IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) , Cusco, Peru, pp.5090-6363, 2017 IEEE .
[11] Shraddha Tadmare, Bodireddy Mahalakshmi, “A Survey on Plant Disease Detection and Classification Using Different Machine Learning Algorithms” , Vol.7, Issue.2, pp.338-341, Feb-2019.
[12] H. Chandrashekhara, M. Suresha, “Classification of Healthy and Diseased Arecanuts using SVM Classifier”, Vol.7, Issue.2, pp.544-548, Feb-2019.

V.D. Kulkarni, S.S. Gaikwad, T.M. Gawade, P.L. Karande, P.A. Umare, "Computer System for Diagnosis of ‘Melanoma’ Type of Skin Cancer Using Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.340-343, 2019.

Now a day’s capturing a live images with high quality plays an crucial role in all the fields. It is more important as far as security in military, commercial, household fields as well as to monitor the continuous changes in earth surfaces are concern. Most of the time to achieve clear images we have to differentiate between original object and shadow as detecting objects under the influence of shadow is a challenging task. In urban area the shadow produces artificial color features and shape deformation of objects which decays the quality of image. Shadow mainly occurs due to elevate objects and If light source has been blocked by some obstacles. However, a lot of shadowed areas in remote sensing images of urban areas have affected the tasks, such as image classification, object detection and recognition. Many times the shadow of image provides direct evidence for existing of large objects. The shadow is used for target recognition, building positioning, height estimation and slope calculation. The presence of shadow in very high resolution images can represent a serious obstacle for their full exploitation. Although shadow provide important visual clues for object shape perception, illumination position, object occlusion. Thus for the correct image interpretation it is important to detect shadow regions and restore their information. So it is very essential to detect the shadow regions and remove it effectively to get useful information with good quality. Significant researchers have been working on to develop the best shadow detection and removal algorithm which produces high accuracy results. Some researchers had implemented several methods and algorithm for detection and removal of shadow with experimental results. So this paper focuses on study of various methods of shadow detection and removal from images.

Shadow detection method, Shadow removal method

[1] Hongya zhang, Kaimin sun, and Wenzhuo li,” Object-oriented Shadow Detection and Removal from Urban High-resolution Remote Sensing Images”, IEEE transactions on Geoscience and Remote Sensing, vol. 52, no. 11, november 2014.
[2] Kuo-Liang Chung, Yi-Ru Lin, and Yong-Huai Huang,”Efficient Shadow Detection of Color Aerial Images Based on Successive Thresholding Scheme”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 2, February 2009.
[3] Aliaksei Makarau, Rudolf Richter, Rupert Müller and Peter Reinartz,” Adaptive Shadow Detection Using a Blackbody Radiator Model”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, June 2011.
[4] P.S.Ramesh, S. Letitia, “A Novel Approach For Shadows Detection And Shadows Removal From High Resolution Satellite Images,” African Journal of Basic & Applied Sciences 9(4):243-250, 2017.
[5] Dong Cai , Manchun Li , Zhiliang Bao,”Study on Shadow Detection Method on High Resolution Remote Sensing Image Based on HIS Space Transformation and NDVI Index”, 18th International Conference on Geoinformatics, 18-20 June 2010.
[6] P. Sarabandi ,F. Yamazaki , M. Matsuoka,”Shadow Detection and Radiometric Restoration in Satellite High Resolution Images”. IEEE International Geoscience and Remote Sensing Symposium, 20-24 Sept. 2004
[7] Luca Lorenzi, Farid Melgani, and Grégoire Mercier,” A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, september 2012.
[8] Ling Zhang , Qing Zhang , Chunxia Xiao,” Shadow Remover: Image shadow removal based on illumination recovering optimization”. IEEE Transaction on Image Processing. Volume: 24 , Issue: 11 Year : 2015.
[9] Danang Surya Candra, Stuart Phinn, Peter Scarth,”Cloud and Cloud Shadow Removal Of Landsat 8 Images Using Multitemporal Cloud Removal Method”, 6th International Conference on Agro Geoinformatics 7-10 Aug. 2017.
[10] Shuang Luo , Huifang Li , Huanfeng Shen,”Shadow Removal Based on Clustering Correction of Illumination Field for Urban Aerial Remote Sensing Images”, IEEE International Conference on Image Processing (ICIP) Year: 2017.
[11] Vertika Jain , Ajay Khunteta,“Shadow Removal for Umbrageous Information Recovery in Aerial Images”. International Conference on Computer, Communications and Electronics (Comptelix) Year: 2017.
[12] Geethu Vijayan , S. R. Reshma , F. E. Dhanya ; S. Anju , Gayathri R. Nair ; R. P. Aneesh,” A Novel Shadow Removal Algorithm using Niblack Segmetation in Satellite Images,” International Conference on Communication Systems and Networks (ComNet) Year: 2016.
[13] Bin Pan ; Junfeng Wu ; Zhiguo Jiang ; Xiaoyan Luo,” Shadow Detection in remote Sensing Images based on Weighted Edge Gradient Ratio”, IEEE Geoscience and Remote Sensing Symposium Year: 2014.
[14] Nan Su ; Ye Zhang ; Shu Tian ; Yiming Yan ; Xinyuan Miao,” Shadow Detection and Removal for Occluded Object Information Recovery in Urban High-Resolution Panchromatic Satellite Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Year: 2016 , Volume: 9 , Issue: 6.
[15] Hongmei Zhu , Jihao Yin , Ding Yuan , Xiang Liu , Guangyun Zhang,”Dem-based shadow detection and removal forlunar craters”. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2016.
[16] Lei Ma , Bitao Jiang , Xinwei Jiang , Ye Tian,” Shadow removal in remote sensing images using features sample matting”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2015.
[17] W. Zhou, G. Huang, A. Tr oy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. E nv., vol. 113, no. 8, pp. 1769–1777, 2009.
[18] Victor J. D. Tsai,” A Comparative Study on Shadow Compensation of Color Aerial Images in Invariant Color Models”, IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, june 2006.
[19] Rafael C. Gonzalez, Richard E.Woods ,”Digital Image Processing”, Dorling Kindersley publisher, India.

Sadhana R. Sonvane, U.B. Solapurkar, "A Review on Shadow Detection and Removal Method from Various Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.344-351, 2019.

Diabetic Retinopathy is brutal eye disease, which is acting as a major cause of blindness in young or middle age population. In this disease there are major chances of losing vision by patient. According to many eye specialists, it is tough to detect this disease in its early stage. If we could able to detect this disease in early stage we can save patient’s vision. For this purpose doctors recommend periodical checking of eyes by specialist. But in country like India, number of specialists available is not at all sufficient for the overall population of the country. It is also a fact that, these specialists are mostly available for city population. In rural areas there is scarcity of eye specialists and testing equipment’s. In this scenario periodical screening programs and automated Diabetic Retinopathy detection can help a lot. Numbers of researchers are attracted towards research on Automatic DR detection. Proposed paper focuses on medical background of DR and comparison of some existing methods for automatic DR detection.

Diabetic Retinopathy (DR), exudates (EXs), microaneurysms (MAs), hemorrhages (HMs)

[1] Fraz, M.M., Jahangir, W., Zahid, S., Hamayun, M.M. and Barman, S.A., “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification,” Biomedical Signal Processing and Control, vol. 35, pp.50-62, 2017.
[2] Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z. and Liang, Y., “Retinal vessel segmentation in colour fundus images using Extreme Learning Machine,” Computerized Medical Imaging and Graphics, vol. 55, pp.68-77, 2017.
[3] W. Zhou, C. Wu, D. Chen, Y. Yi and W. Du, "Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method," in IEEE Access, vol. 5, pp. 2563-2572, 2017.
[4] Amin, J., Sharif, M., Yasmin, M., Ali, H. and Fernandes, S.L., “A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions,” Journal of Computational Science, vol. 19, pp.153-164, 2017.
[5] Leontidis, G., “A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images,” Computers in Biology and Medicine, 2017.
[6] L. Ngo and J. H. Han, "Multi-level deep neural network for efficient segmentation of blood vessels in fundus images," in Electronics Letters, vol. 53, no. 16, pp. 1096-1098, 8 3 2017.
[7] Abbas, Q., Fondon, I., Sarmiento, A., Jiménez, S. and Alemany, P., “Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features,” Medical & Biological Engineering & Computing, pp.1-16, 2017.
[8] W. Zhou, C. Wu, Y. Yi and W. Du, "Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-Feature Classification," in IEEE Access, vol. 5, no. , pp. 17077-17088, 2017.
[9] Mane, V.M. and Jadhav, D.V., “Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images,” Biomedical Engineering/Biomedizinische Technik, vol. 62, no. 3, pp.321-332, 2017.
[10] L. Seoud, T. Hurtut, J. Chelbi, F. Cheriet and J. M. P. Langlois, "Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening," in IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1116-1126, April 2016.
[11] S. W. Franklin and S. E. Rajan, "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images," in IET Image Processing, vol. 8, no. 10, pp. 601-609, Oct. 2014.
[12] Hari, V.S., Raj, V.J. and Gopikakumari, R., “Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy,” Pattern Analysis and Applications, vol. 20, no. 1, pp.145-165, 2017.
[13] S. Roychowdhury, D. D. Koozekanani, S. N. Kuchinka and K. K. Parhi, "Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images," in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 6, pp. 1562-1574, Nov. 2016.
[14] Kumar, P.S., Deepak, R.U., Sathar, A., Sahasranamam, V. and Kumar, R.R., “Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography” Procedia Computer Science, vol. 93, pp.486-494, 2016.
[15] Rahim, S.S., Palade, V., Shuttleworth, J. and Jayne, C., “Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing,” Brain informatics, vol. 3, no. 4, pp.249-267, 2016.
[16]http://www.eagleeyecentre.com.sg/service/diabetic-retinopathy/
[17] https://www.bondeye.com/part-2-diabetes-affect-eyes-roger-t-adler-md/diabetic-retinopathy-2

Saurabh. S. Athalye, Gaurav Vijay, "Survey of Automatic Detection of Diabetic Retinopathy using digital image processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.352-355, 2019.

Recommender Systems is one of the most useful application of machine learning. They are collection of simple algorithms which tend to provide most relevant and accurate data as per user’s requirement. Travel and Tourism domain is one of the important economic area of a nation and recommender systems in this domain would cater to not only the tourists but also to the governments. This paper is a study of the various recommender systems available in the field of travel and tourism.

Point of Interst(POI), Collaborative filtering, Hybrid Filtering, Recommender System, weather condition

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Roopesh L R, Tulasi.B, "A Survey of Travel Recommender System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.356-362, 2019.

Medical pictures and patient`s data are exchanged between various teams to be investigated and assessed by experts who are topographically separated. Any illicit alteration in this data or the event of information loss during the transmission over an uncertain channel may prompt wrong presumptions which may cause an unfavorable impact on patients. Hence, the security of medical images is always been a concern. This security can be provided by the concepts of cryptography. When it is the case of images, an effective security can be provided by using various methods in the domain of Visual Cryptography. In this paper, the various methods used in encrypting the medical images, their efficiency and drawbacks are discussed. A relative analysis based on the performance metrics utilized in these procedures is provided. This article also explores the various key factors and challenges faced in Visual cryptography techniques.

Medical images Encryption, Visual Cryptography, Visual Cryptography Techniques, Medical data security

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C. Punithadevi, G. Shanmugasundaram, B. Thenmozhi, G. Raga, Kreethika Jain, "A Survey on Visual Cryptography Techniques used in Medical Images Encryption," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.363-370, 2019.