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The PID controller is widely used in industries due to its simple design and stable operation. Though PID controller produces a controlled output for stable and unstable systems, the performance of the system under disturbance is poor. To make the PID controller robust under various environments, Particle Swarm Optimization (PSO) algorithm is used to design the controller. The simulation is carried out for a test system of magnetic levitation. The robust in terms of Integral Square Error (ISE) are poor The control of a magnetic levitation system using PSO based PID controller is proposed in this paper. To solve this problem PSO based tuning of PID controller is demonstrated. The results are compared with classical PID controller.

PID controller, PSO, Magnetic Levitation system

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S. Sivananaithaperumal, "Robust PID controller Design using Particle Swarm Optimization for Magnetic Levitation System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.52-55, 2019.

Statistical analysis is used in traditional data mining techniques. But this analysis is less prone to real world scenario. The latest innovations in technology databases contain imprecise & vague data. In the field of data mining, handling such data is always a tedious task. During important decision making task the use of imprecise data causes the inconsistency & vagueness. In this paper to handle uncertain data in data mining various mathematical models like fuzzy set, soft set, rough set & vague set are projected. Various productive approaches have already renewed the Association rule mining. Comparative study of various models defines the idea for using particular set theory. To deal with commercial management & business decision making problem, for generating profitable patterns here we are trying to explore the concept of different set theory. These are also the main benefits of this paper.

Data mining, Vagueness, uncertainty, fuzzy set, vague set, Gray set, rough set & association rule mining

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Monika Dandotiya, Mahesh Parmar, "A Survey in Data Mining Prospective for handling Uncertainty and Vagueness," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.56-61, 2019.

Every device including the state-of-the-art technologies heavily rely on computers to store and process information. Such a feat was achieved with the evolution of cloud computing. It gave consumers the ability of remote execution as well as reducing the complexity of managing the resources. The introduction of Edge computing consisting of Fog and Mist addressed the real-time application-oriented problems and provided with quick onsite solutions. Both Cloud and Edge are quite different and address different problem statements. This paper merges both the computing architectures to form a hybrid computing architecture named Fluid Computing. Fluid Computing is a combination of Mist, Fog and Cloud computing. The Fluid takes advantage of Edge by collaborating it with Cloud where the need for real-time solution provider was much needed. The paper discusses the interoperability between the three computing paradigms and solves their individual flaws by a collective effort, eventually giving rise to fluid computing. This is done by considering a use-case scenario, comparing and contrasting various feature aspects and visualizing the implementation of the technology behind it. The paper also paves the way for implementing machine learning, artificial intelligence in existing models to build smarter devices.

Fluid Computing, IoT, Mist Computing, Fog Computing, Cloud Computing, Edge Computing, Gateway, Thinnect

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Bishal Ranjan Swain, Jeevan Jyoti Sahoo, Ashutosh Prasad, D. Thamizh Selvam, "Rise of Fluid Computing: A Collective Effort Of Mist, Fog and Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.62-69, 2019.

Naskh and Nastalique text recognition are a challenging task in the Pattern Recognition field because of the cursive and context sensitive nature of the script. Many languages use Naskh or/and Nastalique style for writing. Due to the complexities associated with these writing styles, not much effort has been done for the development of real-time recognition systems for Naskh and Nastalique writing style languages. Traditional recognition process segments the text image into characters for subsequent OCR phases which is less accurate for Naskh/Nastalique text and reduces the accuracy of the recognition system. Recently, Recurrent Neural Network (RNN) based Long Short Term Memory (LSTM) architecture with Connectionist Temporal Classification (CTC) has shown a remarkable result in text image recognition. This paper presents the recognition challenges in the Naskh and Nastalique writing style text and a study of different deep learning techniques applied for the recognition of Naskh Arabic and Nastalique Urdu text.

Naskh, Nastalique, Recognition Challenges, RNN, LSTM

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Shanky Goel, Gurpreet Singh Lehal, "Deep Learning Techniques for Naskh and Nastalique Writing Style Text Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.70-76, 2019.

The uniqueness of retinal microvasculature is that it is the only part of human circulation that can be directly visualised non-invasively in vivo and readily photographed. Developments in fundus image processing over the past 20 years includes advancement being made towards developing automated detection for conditions, such as diabetic retinopathy, age-related macular degeneration and retinopathy of prematurity. Features of retinal blood vessels, microaneurysms, exudates and the hemorrhages are extracted to detect the Diabetic Retinopathy (DR) in the early stages. Diabetic Retinopathy results fluid leaks from retinal blood vessels leading to vision loss. Microaneurysms appear as small circular dark spots on the surface of the retina. The appearance of red and yellow lesions on retina is exudates and hemorrhages. Image processing algorithms can be used to reduce the workload of ophthalmologist and play a vital role in quality assurance tasks. Feature extraction is the first step in developing these automated algorithms for detecting retinal pathologies. Here we review numerous early studies that used for automatic detection of these features. Most of the literature has differences in the method used to evaluate their algorithms or the dataset used, which makes it difficult to compare any two algorithms together. Our study reveals that even though a large number of feature extraction technique are available there is still scope for more accurate algorithms which will work with High Resolution Fundus (HRF) images also.

Diabetic retinopathy, Exudates, Hemorrhages, Micro aneurysms

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[42] K. Adem, “Exudate detection for diabetic retinopathy with circular hough transformation and convolutional neural networks,” Expert Systems with Applications, vol. 114, pp.289–295, 2018.
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[45] X. Zhang and O. Chutatape, “Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2. IEEE, 2005, pp. 422–428.
[46] M. Larsen, J. Godt, N. Larsen, H Lund Andersen, A. K. Sjolie, E. Agardh, H. Kalm, M. Grunkin, and D. R. Owens, “Automated detection of fundus photographic red lesions in diabetic retinopathy,” Investigative ophthalmology & visual science, vol. 44, no. 2, pp. 761–766, 2003.
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Noushira K I, Anil Kumar K.R, Meenakshy K, "A Survey on Feature Extraction Methods in Retinal Fundus Images for Diabetic Retinopathy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.77-87, 2019.

Dispersed processing is pool of organizations that are provided for customer. Conveyed stockpiling data may be gotten to from wherever at whatever point due to cloud works in remote zone. It manhandles the organizations given by the cloud supplier. There are shifting sorts of cloud benefits particularly, PC code as a Service, Platform as an organization, Infrastructure as an organization, Network as a Service, Identity as an organization. Getting ready models of cloud encapsulate Intra cloud, lay cloud, arrange Cloud and lay cross cloud. By virtue of the organizations of disseminated registering, there`s a gigantic measure of learning hold tight cloud .Hence it`s required to supply the acceptable security to the information in Clouds. Consequently the conveyed stockpiling has expanded additional thought from each the teachers and mechanical shared attributes. It likewise gets new troubles keeping up learning genuineness and unwavering quality in data accumulating .Deviating the cloud from single to multi-cloud is imperative to accomplish the information security. Multi cloud is very proposed appreciation to the soundness that fragile data shouldn`t be dispatched to one cloud, to avoid oppression on only one cloud supplier. Data to be held tight is part into varied squares and scattered among absolutely extraordinary disseminated stockpiling suppliers. To deal with all the outline we will when all is said in done gift an audit paper. This diagram paper relies upon the propelled examination related with single and multi-cloud esteem, security and handiness based generally describe. This work mean to drive crafted by multi spread over single cloud to decrease the vulnerability demanding inside the cloud

Cloud Computing, Data Privacy, Multi-Clouds, Security, Confidential Data, Cloud Service Provider

[1] Emir Erdem and Mehmet Tahir Sandıkkaya , Member, IEEE,” OTPaaS—One Time Password as a Service”, IEEE Transactions On Information Forensics And Security, Vol. 14, No. 3, March 2019.
[2] Yucai Zhou ; Kejun Long ; Peng Xu ; Wang Liu, ,” Design of a Cloud Platform for Digital Highway”, International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), March,2019.
[3] Himel Dey , Rifat Islam ,Hossain Arif,” An Integrated Model To Make Cloud Authentication And Multi-Tenancy More Secure”, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), February,2019.
[4] Yucai Zhou, Kejun Long ,Peng Xu , Wang Liu, ,” Authorized Equality Test on Identity-Based Ciphertexts for Secret Data Sharing via Cloud Storage”, IEEE Journals & Magazines, volume 7, Page s: 25409 – 25421, February,2019.
[5] Andrei Tchernykh ,Mikhail Babenko , Vanessa Miranda-López, Alexander Yu. Drozdov , Arutyun Avetisyan,” WA-RRNS: Reliable Data Storage System Based on Multi-cloud 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW),August,2019.
[6] Prof. Amit R. Gadekar, Dr. M V. Sarode, Dr. V M. Thakare,” Cloud Security and Storage Space Management using DCACrypt”, International Conference on Information, Communication, Engineering and Technology (ICICET),August,2018
[7] Jayachander Surbiryala, Bikash Agrawaly, Chunming Rong,” Improve Security over Multiple Cloud Service Providers for Resource Allocation”, 1st International Conference on Data Intelligence and Security,2018
[8] Talal Halabi, Martine Bellaiche, and Adel Abusitta,” Online Allocation of Cloud Resources based onSecurity Satisfaction, 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering”,2018
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[12] Mu Yang, Andrea Margheri, Runshan Hu, and Vladimiro Sassone, “Differentially Private Data Sharing in a Cloud Federation with Blockchain”, Copublished by the IEEE CS and IEEE ComSoc,November/December 2018.
[13] Mohamad Reza Khayyambashi,Sayed Mohammad Hossein, Mirshahjafari, EhsanShahrokhi, , “Design And Implement A New Cloud Security Method Based On MultiClouds On Open Stack Platform” ,David C. Wyld et al. (Eds) : CCSEA, CLOUD, DKMP, SEA, SIPRO – 2016.
[14] C. Divya Shaly, R. Anbuselvi, ” Multi-Cloud Data Hosting for Protection Optimization and Security”-International Journal of Computer Science and Mobile Computing. April 2016.
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[16] Ms Vrushali K Gaikwadl, Prof. Ramesh Kagalkar. “Data Security & Availability in Multi-clouds Storage with Cooperative Provable Data Posession”.–IJES February 2015.
[17] Yun Tian, Xiao Qin, Yafei Jia.” Secure Replica Allocation in Cloud Storage with Heterogeneous Vulnerabilities”,IEEE
[18] Tara Salman. “On Securing Multi-Clouds: Survey on Advances and Current Challenges”, Nov 2015.
[19] Ms Vrushali K Gaikwadl, Prof. Ramesh Kagalkar. “Data Security & Availability in Multi-clouds Storage with Cooperative Provable Data Posession”.–IJES February 2015.
[20] Maha Tebaa, Said El Hajji, ” From Single to Multi-Clouds Computing Privacy and Fault Tolerance”. Elsevier 2014.
[21] P. Raja Kaushik1 , G. Praveen Bab,” Data Security Issues in Distributed Cloud System”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.9, September- 2014, pg. 357-360.
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[27] A.Bessani, M. Correia, B.Quaresma, F.Andre and P.Soura “Depsky:dependable and secure storage in cloud computing.”2011.
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S. Renuka, N. Suresh Kumar, "A Survey on Cloud Data Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.88-95, 2019.

Diabetes often leads to several secondary ailments related to different parts of the body. One such medical disorder is Diabetic Retinopathy. In this condition, the lipids accumulate in the retinal areas of the eye due to fragile blood vessels. These depositions are called Exudates. If exudates are not detected and treated well within the time, they can cause permanent blindness. This paper focuses on a method that can detect the presence of exudates.

Diabetic Retinopathy, Exudates, Image Processing, Contrast Adjustment

[1] Nathan Congdon, Yingfeng Zheng “The WorldWide Epidemic of Diabetic Retinopathy”, Indian Journal of Ophthalmology, Vol.60, Issue.5, pp.428-431, 2012.
[2] Kristen Harris, Nidhi Talwar, William H. Herman, “Predicting Development of Proliferative Diabetic Retinopathy”, American Diabetes Association Diabetes Care, America, pp. 562-568, 2013.
[3] Ravitej Singh Rekhi, Ashish Isaac, Malay Kishore Dutta, Carlos M. Travieso, “Automated Classification of Exudates from Digital Fundus Images”, In the Proceedings of the 2017 IEEE International Conference, India, 2017.
[4] Narendra P. Datti, Ashwini Mahajan, Shalini, Rashmi N. R., “Diabetic Retinopathy: How Aware are the Physicians?”, Journal of Evolution of Medical and Dental Science, Vol.3, Issue.20, 2014.
[5] P. R. Asha, S. Karpagavalli, “Diabetic Retinal Exudates Detection using Machine Learning Techniques”, In the Proceedings of the 2015 International Conference on Advanced Computing and Communication Systems ICACCS, Coimbatore, India, 2015.
[6] Mohammed Shafeeq Ahmed, Baddam Indira, “Detection of Exudates from RGB Fundus Images using 3-Sigma Control Method”, In the Proceedings of the 2017 IEEE WiSPNET Conference, 2017.
[7] Priyadarshini Patil, Pooja Shettar, Prashant Narayankar, Mayur Patil, “An Efficient Method of Detecting Exudates in Diabetic Retinopathy: using Texture Edge Features”, In the Proceedings of the 2016 International Conference on Advances in Computing Communications and Informatics ICACCI, Jaipur, India, 2016.
[8] S R Rupanagudi, “A Novel Video Processing based Cost Effective Smart Trolley Systems for Super Markets using FPGA”, In the Proceedings of the 2015 International Conference on Communication Information and Computing Technology ICCICT, Mumbai, pp.1-6, 2015.
[9] S R Rupanagudi, “A High Speed Algorithm for Identifying Hand Gestures For an ATM Input System for the Blind,” In the Proceedings of the 2015 IEEE, Mumbai, pp.1-6, 2015.
[10] C R Prashant, T Sagar, N Bhat, D Naveen, S R Rupanagudi, R A Kumar, “Obstacle Detection and Amp: Elimination of Shadows for an Image Processing Based Automated Vehicle”, In the Proceedings of the 2013 International Conference on Advances in Computing Communication and Informatics, Mysore, pp.367-372, 2013.
[11] S R Rupanagudi, “A Novel Video Processing based Smart Helmet for Rear Vehicle Intimation and Collision Avoidance”, In the Proceedings of the 2015 International Conference on Computing and Network Communications, Trivandrum, pp.799-805, 2015.

Srujani J, K. Pramilarani, "An Image Processing Algorithm to Detect Exudates in Fundus Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.96-99, 2019.

Automatic identification of human face has done a significant research interest and lead to many interesting applications. Identifying faces of human is a challenging problem due to the huge variation in the appearance of each character. In this paper, we are representing the problem of identifying characters in movies or TV serials using video and film script. We are attempting to extract frames from videos and clustering the faces from frames. System will extract names from script using HTML parser. A graph structure of character name and face get generated and matching between names and clustered face tracks under the some circumstances. In complex movie scenes, during the face tracking and face clustering process, noise gets generated and due to this, the performance of the system may get limited. This paper focuses on implementation of graph structure for movie script and movie character, generating a relationship also matching this graph for identification of movie characters role.

Character Identification,K-means Clustering, ECGM, graph edit, graph matching

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[6] Y. Zhang, C. Xu, H. Lu, and Y. Huang, “Character identification in feature-length films using global face-name matching,” IEEE Transaction on Multimedia , vol. 11, no. 7, pp. 1276–1288, November 2009.
[7] M. Everingham, J. Sivic, and A. Zissserman, “Taking the bite out of automated naming of characters in TV video”, in Journal of Image and Vision Computing, 2009, pp. 545559.

S. S. Deore , "Role Identification in Movie using ECGM," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.100-104, 2019.

Artificial intelligence, with the emergence of machine learning and deep learning techniques, is growing up with breath neck speed. With the evaluation of the deep convolutional neural network, applications like image classification, object recognition and detection become easier. Recently, a new network deep learning architecture named Capsule Network is introduced to overcome some spatial and rotational limitations of CNN by using the concepts of capsules and the dynamic routing algorithm. Capsules are a group of neurons that generates activity vector whose length predicts the class of image and the orientation defines the pose parameters related to the image. Capsule networks have resulted in state of the art performance on various dataset such as MNIST. The paper defines the architecture and working of the capsule network, along with the comparative analysis of CNN and Capsule network on the various dataset. Along with this, the paper specifies the hands-on experiments done on capsule networks and the future scope with capsule networks.

Capsule networks, convolutional neural networks, deep learning, dynamic routing algorithm, image classification

[1] K. O`Shea, R. Nash. "An introduction to convolutional neural networks." arXiv preprint arXiv:1511.08458 (2015).
[2] S. Sabour, N. Frosst, and GE. Hinton. "Dynamic routing between capsules." In the Proceedings of 2017 NIPS Conference on Advances in neural information processing systems, pp. 3856-3866. 2017.
[3] GE. Hinton, A. Krizhevsky, SD. Wang. "Transforming auto-encoders." In International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, pp. 44-51, 2011.
[4] GE. Hinton, S. Sabour, N. Frosst. "Matrix capsules with EM routing." Open review (2018).
[5] P. Nair, R. Doshi, S. Keselj. "Pushing the limits of capsule networks." Technical note (2018).
[6] R. LaLonde, U. Bagci. "Capsules for object segmentation." arXiv preprint arXiv:1804.04241 (2018).
[7] M. Engelin. "CapsNet Comprehension of Objects in Different Rotational Views: A comparative study of capsule and convolutional networks." (2018).
[8] A. Gulli, and S.Pal. “Deep Learning with Keras”. Packt Publishing Ltd, Birmingham - Mumbai, pp. 71-105, 2017.

Anusha Mehta, V. D. Parmar, "A Study on Capsule Networks with the Comparative Analysis of Capsule Networks and CNN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.105-108, 2019.

Our goal is to enhance the images so that output is better than original image. We used digital image enhancement technique that offers choices for enhancing the vision of images. We will describe a summary of existing concepts with multiple algorithms for image enhancement. In this paper we emphases on many techniques such as Piecewise Linear Transformation Function, Histogram Equalization and Histogram Matching with Statistical approach that completely enhanced our images with good contrast and matching. We use local enhancement method to obtain the histogram of the image with various intensities. In this method we can easily compare pixel values of previous histogram to obtain new histogram. We are not getting good results with our previous techniques, so that we are proposing our new approach Piecewise Linear Transformation Using Histogram Processing. In this approach we are applying many functions randomly with histogram processing for contrast enhancement so that we achieve a good or enhanced image.

DigitalImage processing, gray scale operation, image enhancement, Piecewise Linear Transformation Function, Histogram processing

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Indu Sharma, V.K Panchal, "Piecewise Linear Transformation Function Using Histogram Processing for Image Enhancement," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.109-112, 2019.