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Indian sign language (ISL) could be a language which is used to employ by hearing and speech impaired individuals to speak with other individuals. In this paper we present system which might recognise hand poses and gestures from the Indian sign language (ISL) in real-time mistreatment grid-based options. Here, we are introducing hand gesture recognition system to recognize the gestures and convert them to a natural language. Gesture recognition can be used to communicate merely through gestures without any physical link with the actual machine. Gesture is converted to text which helps deaf-dumb people to communicate with normal people. The system can be programmed in such a way that it can translate gesture to text. The proposed system involves taking the input through the in-built camera. One of the advantage of the system is that the individual can add new sentences based on their comfort and understanding. The output text is displayed on the screen based on the gesture showed to the camera.

Hand guesture recognition, unicode, image processing, webcam

[1] Joyeeta Singha, Karen Das “Recognition of Indian Sign Language in Live Video”, IJCA Transaction, Vol.70,Issue.19,pp.17-22,2013.
[2] Sakshi Lahoti, Shaily Kayal, Sakshi Kumbhare, Ishani Suradkar, Vikul Pawar1 S. Willium, “Android based American Sign LanguageRecognition System with Skin Segmentation and SVM”, IEEE Transaction, IEEE Transaction,pp.1-6, 2018.
[3] Nico Zengeler, Thomas Kopinski, Uwe Handmann “Hand Gesture Recognition in Automotive Human–Machine Interaction Using Depth Cameras”, MDPI Transaction, pp.1-28, 2012.
[4] Anant Agarwal, Manish K Thakur “Sign Langauage Transaction using Microsoft Kinect “, IEEE Transaction, pp.181-185.
[5] Zaher Hamid Al-Tairi, Rahmita Wirza Rahmat, M. Iqbal Saripan, Puteri Suhaiza Sulaiman, “Skin Segmentation Using YUV and
RGB Color Spaces”,KIPS Tranaction,pp.283-299.
[6] Houssem Lahiani, Mohamed Elleuch, Monji Kherallah, “Real Time Hand Gesture Recognition System forAndroid Devices”, IEEE Transaction,pp.591-596.
[7] Prajwal Paudyal, Ayan Banerjee, and Sandeep K.S. Gupta, “SCEPTRE: a Pervasive, Non-Invasive, and Programmable Gesture Recognition Technology”,Impact Transaction,pp.1-12.
[8] Dipali Rojasara, Nehal Chitaliya, “Real Time Visual Recognition of Indian Sign Language using Wavelet Transform and Principle Component Analysis”,IJSCE Transaction,Vol.4,Issue.3,pp.17-20.
[9] Pooja Kiranalli, Dr. S. R. Gengaje, “Real time Indian Sign Language Number Recognition System”,IJRTER Transaction,Vol.2, Issue.6;pp.200-205.
[10] Kumud Tripathi, Neha Baranwal and G. C. Nandi, “Continuous Indian Sign Language Gesture Recognition and Sentence Formation”,IMCIP Transaction, pp.523-529.
[11] Kartik Shenoy, Tejas Dastane, Varun Rao, Devendra Vyavaharkar, “Real-time Indian Sign Language (ISL) Recognition”, IEEE Transaction,2018
[12] Kartik Shenoy, Tejas Dastane, Varun Rao, Devendra Vyavaharkar, “Real-time Indian Sign Language (ISL) Recognition”,IEEE Transaction,2018,pp.1-9

Rakesh.B.S, Tamilarasan.S, Avinash N, "Hand Gesture Recognition based on Real-time Indian Sign Language," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.181-185, 2019.

This design, implementation, and evaluation of a research work for developing an automatic person identification system using hand written biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment. . In order to train and test the developed automatic person identification system, an in-house hand written database is created, which contains hand signatures of different persons . The collected hand data have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system. For features training and classification, the support vector machine(SVM) is used.

SVM,handwritingrecognition,offline,online,static

[1].Naoya Wada ; “HMM Based Signature Identification System Robust to Changes of Signatures” with Time 2007 IEEE Workshop on Automatic Identification Advanced Technologies7-8 June 2007.
[2].A.A.M.Abushariah ; T.S. Gunawan ; J. Chebil ; M.A.M. Abushariah “Automatic person identification system using handwritten signatures” 2012 International Conference on Computer and Communication Engineering (ICCCE)3-5 July 2012.
[3].hifzan ahmed -shailjashukla ; hari mohan rai static “handwritten signature recognition using discrete random transform and combined projection based technique ”2014 fourth international conference on advanced computing & communication technologies.
[4].Parashuram- Chandrashekar Gudada Restoration of degraded “Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniques” 2017 International Conference on Computer Communication and Informatics (ICCCI).
[5]. Susana M. Vieira “Hybrid neural models for automatic handwritten digits recognition ”2018 International Joint Conference on Neural Networks (IJCNN).

Sushma sugandhi, Vinita Patil, "Person Identification Based On Handwriting," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.186-189, 2019.

The wireless sensor networks is the decentralized type of network in which sensor nodes join or leave the when they want. The energy consumption is the major issue of wireless sensor networks due to small size and far deployment of the sensor nodes. The whole network is divided into fixed size clusters and cluster heads are selected in each cluster based on distance and energy. The hierarchal routing is the efficient technique for the data aggregation which uses least network energy. The TH-leach is the improved version of LEACH protocol to increase lifetime of the sensor network. In this research work, the TH-leach routing protocol will be further improved to increase one level to improve lifetime of the network.

EnergyEfficient,LEACH,LEACH-TLCH,TH-LEACH

[1] Fouad El Hajji, Cherkaoui Leghris, Khadija Douzi, “Adaptive Routing Protocol for Lifetime Maximization in Multi-Constraint Wireless Sensor Networks,” 2018, IEEE.
[2] Vandna Arya, “Energy Enhancement of NEAHC Protocol Wireless Sensor Network using Firefly Algorithm,” International Journal of Computer Science Engineering (IJCSE), 2018.
[3] Shahab Tayeb*, Miresmaeil Mirnabibaboli and Shahram Latifi, “Cluster Head Energy Optimization in Wireless Sensor Networks,” doi: 10.13052/jsn2445-9739.2016.008, 2018.
[4] Hajer Ben Fradj, Rajoua Anane, and Ridha Bouallegue, “Energy consumption for opportunistic routing algorithms in WSN,” 2018, IEEE.
[5] Huseyin Ugur Yildiz, Vehbi Cagri Gungor , and Bulent Tavli, “A Hybrid Energy Harvesting Framework for Energy Efficiency in Wireless Sensor Networks Based Smart Grid Applications,” 2018, IEEE.
[6] Satyasen Panda, Sweta Srivastava, Santosh Mohapatra and Priyaranjan Kumar, “Performance analysis of wireless sensor networks using Artificial Bee Colony algorithm,” 2018, IEEE.
[7] Madiha Razzaq, Devarani Devi Ningombam, Seokjoo Shin, “Energy Efficient K-means Clustering-based Routing Protocol for WSN Using Optimal Packet Size,” 2018, IEEE.
[8] Ramin Yarinezhada, Amir Sarabi, “Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink”, 2018, Int. J. Electron. Commun. (AEÜ) 84,144–152.
[9] Hajer Ben Fradj, Rajoua Anane, and Ridha Bouallegue, “Energy consumption for opportunistic routing algorithms in WSN,” 2018, IEEE.
[10] Huseyin Ugur Yildiz, Vehbi Cagri Gungor , and Bulent Tavli, “A Hybrid Energy Harvesting Framework for Energy Efficiency in Wireless Sensor Networks Based Smart Grid Applications,” 2018, IEEE.
[11] Satyasen Panda, Sweta Srivastava, Santosh Mohapatra and Priyaranjan Kumar, “Performance analysis of wireless sensor networks using Artificial Bee Colony algorithm,” 2018, IEEE.
[12] Madiha Razzaq, Devarani Devi Ningombam, Seokjoo Shin, “Energy Efficient K-means Clustering-based Routing Protocol for WSN Using Optimal Packet Size,” 2018, IEEE.
[13] Nazia Suraia Usha, Monir Hossen, and Shuvashis Saha, “Efficient Duty Cycle Management for Reduction of Energy Consumption in Wireless Sensor Network,” 2017, IEEE.

Disha Verma, Er. Sandeep Kumar Rawat, "The Cache Based Scheme To Increase Lifetime of Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.190-194, 2019.

Deep Learning is a system powered by huge amounts of data. With the generation of massive amounts of data, the data analysing keeps getting complex. Deep learning solves the problem of Traditional ML algorithms that fail to perform well when the amount of data is enormous. Deep learning can be applied to any type of data such as text, image and so on. Deep learning algorithms generally used and best suited for image data are DBN and CNN. Analysing Computer vision using CNN brings a lot of use cases such as detection, recognition from the images, which can be useful in many fields such as medical images to detect a tumour and recognize its type, or help a robot navigate by identifying obstacles. In this paper we discuss what is Artificial Intellignece(AI), Machine Learning(ML) and Deep Learning and explore some of the Deep learning algorithms. We also understand how CNN can be applied in different applications of Computer vision and study the three major applications of Computer vision which are Image captioning, Medical image analysis and Robots Navigation.

AI, ML, Computer Vision, DBN, CNN, RNN

[1] T. M. Mitchell, “Machine Learning”, McGraw Hill Education; First edition, New York, USA
[2] S. Rajaraman, S. Candemir, Z. Xue, P. O. Alderson, M. Kohli, J. Abuya, G. R. Thoma, and S. Antani, “A novel stacked generalization of models for improved TB detection in chest radiographs”
[3] B. Xu, Y. Chai, C. M. Galarza, C. Q. Vu, B. Tamrazi, B. Gaonkar, L. Macyszyn, T. D. Coates, N. Lepore, and J. C. Wood, “Orchestral Fully Convolutional Networks forsmall lesion segmentation in Brain MRI”
[4] S. Shabir, S. Y. Arafat, “An image conveys a message: A brief survey on image description generation”
[5]I. Reda, B. O. Ayinde, M. Elmogy, A. Shalaby, M. El-Melegy, M. A. El-Ghar, A. A. El-fetouh, M. Ghazal, A. El-Baz, “A new CNN based system for early diagnosis of Prostate Cancer”
[6] C. C. Park, B. Kim, and G. Kim, “Towards Personalized Image Captioning via Multimodal Memory Networks”
[7] Q. Wu , C. Shen , P. Wang, A. Dick, and A. v. d. Hengel, “Captioning and Visual Question Answering Based on Attributes and External Knowledge”
[8] M. Vicky, G. Aziz, H. Hindersah, “Implementation of Vehicle Detection Algorithm for Self-Driving Car on Toll Road Cipularang using Python Language”
[9] C. Lin, J. Lu, J. Zhou, “Multi-Grained deep feature Learning for Pedestrian detection”
[10] S. Hussain, M. Abualkibash, S. Tout, “A of Traffic Sign Recognition Systems Based on Convolutional Neural Networks”
[11] C. Amritkar, V. Jabade, “Image Caption Generation using Deep Learning Technique”
[12] S. Shabir, S. Y. Arafat, “An image conveys a message: A brief survey on image description generation”
[13] Boya Akhila, Burgubai Jyothi, “Face Identification through Learned Image High Feature Video Frame Works”
[14] N.S. Lele, “Image Classification Using Convolutional Neural Network”

Savita K Shetty, Ayesha Siddiqa, "Deep Learning Algorithms and Applications in Computer Vision," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.195-201, 2019.

Landslides play an important role in this world. The landslide affects hundreds and thousands of people and is economically vulnerable. The causes of landslides are mainly caused by rain, earthquake and so on. This paper helps to use the method for classification of landslide detection. Subsequently, the construction of the classification algorithm depends on the global-landslide dataset. The main purpose of this method to improve the performance of machine learning ensemble classifiers is to perform better in terms of classification accuracy and execution time based on multiboost, bagging, subspace discrimination and subspace KNN.

landslide, clustering techniques, Machine Learning, k-means, ensemble classifiers

[1] Maneesha V. Ramesh, “Real-time Wireless Sensor Network for Landslide Detection”, 2009 Third International Conference on Sensor Technologies and Applications, 2009.
[2] Yang Hong, Robert F. Adler, and George Huffman,“An Experimental Global Prediction System for Rainfall-Triggered Landslides Using Satellite Remote Sensing and Geospatial Datasets”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 45, No. 6, June 2007.
[3] S.Karthik, K.Yokesh, Y.M.Jagadeesh, R.K.Sathiendran, ”Smart Autonomous Self Powered Wireless Sensor Networks based Low-cost Landslide Detection System” 2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT], 2015.
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[10] N. L. Ravi Teja1, V.K.R. Harish, D. Nayeem Muddin Khan ,R. Bhargava Krishna, Rajesh Singh, S Chaudhary, “Land Slide Detection and Monitoring System using Wireless Sensor Networks (WSN)”, IEEE 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India, 2014.
[11] Mohammad Zawad Ali, Md Nasmus Sakib Khan Shabbir, Xiaodong Liang, Yu Zhang, Ting Hu, ”Machine Learning based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals”, IEEE Transaction, Vol.55, Issue.3, pp.2373-2391, 2019.
[12] Satishkumar Chavan, Shobha Pangotra, Sneha Nair, Vinayak More, Vineeth Nair, ”Effective and Efficient Landslide Detection System to Monitor Konkan Railway Tracks” 2015 International Conference on Technologies for Sustainable Development (ICTSD-2015), 2015, Mumbai, India.
[13] Huang Qingqing, Meng Yu, Chen Jingbo, Yue Anzhi, Lin Lei, “Landslide Change Detection Based On Spatio¬temporal Context”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017.
[14] Siti Khairunniza-Bej, Siti Rusaniza Jusoh, ”Integrated change detection method for landslide monitoring”, International Conference on signal Acquisition and Processing, 2009.
[15] Thomas L., “A Scheme to Eliminate Redundant Rebroadcast and Reduce Transmission Delay Using Binary Exponential Algorithm in Ad-Hoc Wireless Networks”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.1-6, 2017.
[16] Gagandeep Kau , Harmanpreet Kaur, “Ensemble based J48 and random forest based C6H6 air pollution detection “, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.41-50, April (2018).
[17] Rohini M , Arsha P, “Detection of Microaneurysm using Machine Learning Techniques”, International Journal of Scientific Research in Network Security and Communication, Volume-7, Issue-3, Jun 2019.
[18] Hemant Kumar Soni, “Machine Learning-“A New Paradigm of AI”, International Journal of Scientific Research in Network Security and Communication, Volume-7, Issue-3, Jun 2019.
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R.Sindhuja, A. Padmapriya, "Landslide Detection of Using Ensemble Classifiers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.202-206, 2019.

Biometric technology is becoming a standard security feature in large businesses. In the modern networked society, there is an ever growing need to determine or verify the identity of a person. Where authorization is necessary for any action, be it collecting a child from child-care facilities or boarding an aircraft, authorization is almost always vested in a single individual or a class of individuals. There are a number of existing methods, used by society or automated systems to verify identity. Traditional existing methods can be grouped into three classes: (i) possessions; (ii) knowledge and (iii) biometrics. Biometrics is the science of identifying or verifying the identity of a person based on physiological or behavioral characteristics. It provides additional layer of security via its strong authentication process. This innovation allows compromised premises to quickly identify intruders. As the technology world is evolving there are more and more trends and demand in the field of identity management. All these trends and demands are generated from one basic need – the need for a more accurate and secure way of identifying an individual. The intelligent ones are already learning to adopt with these trends in order to gain competitive advantage. This Paper brings about the recent trends in Biometric Technologies and its applications.

Wi-Fi, SSO, Cryptography, Cloud-Biometrics etc

[1]. B.Miller, “Vital Signs of Identity”. IEEE Sectrum , 22-30, 1994.
[2]. D.Gayathri, R.Uma Rani, “A Prototype for Secure Web Access Model using Multimodal Biometric System based on Fingerprint and Face Recognition”, International Journal of Computer Science and Information Technologies ,vol. 3,issue. 3,pp. 3985-3988, 2012.
[3]. D.Gayathri, R.Uma Rani,” An Efficient Multimodal Biometric System Using Adaptive Gabor Filtering based Feature Extraction”, European Journal of Scientific Research ,vol. 141,issue. 4,pp. 463-475, 2016.
[4]. D.Gayathri, R.Uma Rani,” Multimodal Biometric System: An Overview”, International Journal of Advanced Research in Computer and Communication Engineering ,vol.2,issue 1,pp. 898-902, 2013.
[5]. K.Modi, S. “Biometrics in Identity Management Concepts to Applications”.
[6]. Meenakshi Gupta, Adti Grover, “Survey on Technique of Multimodal Biometric System”, International Journal of Innovative Research in Computer and Communication Engineering , 1-7, 2016..
[7]. Nalini K. Ratha, Andrew Senior and Ruud M.Bolle. “Automated Biometrics”, pp. 1-11.
[8]. S.Khellat-Kihel,” Finger Vein recognition using Gabor filter and support vector Machine in :IPAS”, 2014.
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D. Gayathri, R. Uma Rani, "An Overview of Biometric Technologies and Its Benefits," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.207-213, 2019.

Complex Optimization problems are solved effectively by heuristic approaches. Bio-inspired algorithms play a vital role in solving various complex computational problems. Ant Colony Optimization is one such bio-inspired techniques that stand even after three decades in addressing such issues. Travelling Salesman Problem is taken as a case study and an enhanced ant colony algorithm is used to solve it using the best feature of ACO called as pheromone. The proposed technique involves a modified pheromone rule plays a key parameter in solving the TSP. The experimental medium takes a graph of cities and their distances, followed by solving it using the proposed approach. The data analytics of maximum, mean and minimum costs of the graph are analyzed using R tool. The experimental results prove that the proposed enhanced ant colony algorithm effectively solves Travelling Salesman Problem.

Optimization, Travelling Salesman Problem, Bio-Inspired, Ant colony Algorithm, Pheromone

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S. Suriya, M. Rahul Kumar, "Solving Travelling Salesman Problem using an Enhanced Ant Colony Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.214-222, 2019.

As Cloud computing emerges as a dominant paradigm in distributed systems, it is important to fully understand the underlying technologies that make clouds possible. One technology, and perhaps the most important, is the virtualization. Recently virtualization, through the use of hypervisors, has become widely used and well understood by many. Distributed Denial of Service (DDoS) attacks typically focus high quantity of IP packets at specific network entry elements; usually any form of hardware that operates on a Blacklist pattern is quickly overrun. As in the cloud computing, a large number of tenants or VM clients share the common hardware, DDoS attacks have the potential of having much greater impact than against single tenanted architectures. In this paper, various secure virtualization schemes and preventions from DDoS flooding zombie attacks are analyzed. Security threats are discussed and preventive measures from DDoS attacks and protection solutions are presented so that the companies can take the appropriate action accordingly.

DDoS, flooding, attack, Cloud, recovery, prevention, secure, virtualization

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[4]. Ms Jayshri Damodar Pagare, Dr. Nitin A Koli, “A technical review on comparison of Xen and KVM hypervisors: An analysis of virtualization technologies”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 12, December 2014, ISSN (Online) : 2278-1021 , ISSN (Print) : 2319-5940, pp. 8828-8832
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Amit K. Chaturvedi, Punit Kumar, Kalpana Sharma, "Analyzing the Secure Virtualization schemes and preventions from DDoS flooding Zoombie attacks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.223-229, 2019.

In the modern times digital image processing technology used by the end users has been in the interest as it provides the easy solution to the complicated issues. Like face recognition, image classification etc. We have proposed the concept of blood group type detection using image processing techniques based on the input images. It will be very difficult to detect the type of the blood to any end user. The need of the accurate detection is high in disaster situation where no lab or expert persons are available to detect the type of it. Hence we have proposed a pixel cluster based analysis of the blood type based on the Region Adjacency Graphs (RAG) and Super Resolution Mapping (SRM) with pixel analysis and Region of interest (ROI) based image segmentation. Later the use of neural network will help to classify the image based on the pixel analysis features. The proposed system results were obtained by using MATLAB. Successful results were obtained and accuracy of the proposed system is most desirable.

Blood type detection, Image segmentation, Pixel analysis, Neural Network

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Bhavana R. Maale, Soumya, "ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.230-234, 2019.

In the competitive situation, different methods of Multi-criteria decision support have been used to help decision maker select better alternatives for various decision problems. To an economic consideration, there are several criteria needed to be taken into account the temperatures in different cities in the World. According to that base we recorded the temperature in the different months in a city in the year 2018. In this paper, we presented the warmth temperature in the city and crisp temperature of the city by account of the decision making processes. To solve complex real-world decision making problems, multi-attribute decision making (MADM) methods have been developed. The TOPSIS is among the most widely used methods at present which provides valuable outputs in different application areas. With the above hypotheses, calculations involving Eigen vector, square rooting and summations are used for obtaining a relative closeness value of the criteria tested. TOPSIS ranks these values of relative closeness of the whole system by assigning the highest value of the relative closeness to the best attributes in the system. A numerical example given to illustrate the solution process of the suggested approach.

Crisp, DM, MADM, MCDM, TOPSIS, Warmth

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P.K. Parida, "A TOPSIS Approach for Ranking Warmth Service Providers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.235-240, 2019.