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Mobile devices, which are used by almost everyone for both private and professional intention, have become an indispensable part of people’s life. Today, one of the most important and primary features for smartphone users is that they communicate with each other through social media applications. Used by billions of people, these applications provide a lot of personal information about its user. Mobile device forensic is a significant subset of digital forensic. It is concerned with the acquisition and analysis of digital evidence on mobile devices. Commonly used by many people for messaging, calling and sharing photos/videos, WhatsApp Messenger contains several evidences for mobile forensic and is an important data mine for forensic professionals. In this study, WhatsApp Messenger, which is the most widely used instant messaging application in the world, was examined in terms of digital forensic by using popular forensic tools Cellebrite UFED and Magnet AXIOM on iPhone 5s (A1457) model smartphone with iOS operating system. Sent, received and deleted data (messages, images, videos, calls, etc.) obtained from WhatsApp Messenger application’s files, databases and logs in the internal memory of the smartphone was analysed. The acquired results were given comparatively with details. The results and comparison of current commercial forensic software will help forensic experts in subsequent data analysis.

Mobile Device Forensic, iOS Forensic, Instant Messenger Applications, WhatsApp Messenger, Forensic Tools

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Ziya UYSAL, Ilyas ÇANKAYA, Baha SEN, "Forensic Analysis of WhatsApp Messenger on iOS Smartphones," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.1-10, 2020.

Exploiting the computation potential of multi-core Graphics Processing Units (GPUs) requires reducing memory access latency and memory transfer overheads. Although the GPUs provide fast processing capabilities, the memory on such devices is significantly less. Though the memory hierarchy of GPUs provide certain fast levels, these are limited in terms of storage. Also, in order to use the GPUs for computation, the data must be transferred into the memory of the same; hence, in order to reduce the memory latency for large volume of data transfer, efficient techniques are required. Analysis of graph data on GPUs have many practical applications, and has been studied by both academia and industry. The graph data must be stored on the GPU memory to perform computation and analysis on the same. There are different data structures that can be used to store graphs in the GPU memory. Employing compression techniques to reduce the size required by the data is useful; however, the computation must be performed on the compressed data itself since decompressing the data would not be feasible. In addition to saving space on the device, using compressed data structures also reduces the memory transfer overheads both between the CPU & GPU, and between the various levels in the memory hierarchy of the GPU, thereby compensating for some of the additional time to retrieve information from the compressed data. Storing data using efficient compression techniques and operating on the compressed data is therefore useful. Quadtree data structures are generally used for storing and representing images for various applications. However, graphs when represented as adjacency matrix are comparable to images; hence, using recursive partitioning techniques, the data can be effectively compressed. In this paper, we show techniques based on quadtrees to efficiently compress graph data for storing and computation on GPUs. Additional techniques are also introduced which result in hybrid data structures that perform better for specific cases. Empirical results show 80-90% decrease in the space requirements to store graphs with real-world properties.

Compression, Quadtree, Graph Compression, GPUs

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Amlan Chatterjee, "Compressing Graphs Using Quadtrees for Efficient Computation on GPUS," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.11-18, 2020.

Intrinsic Multiple Email Service Full MIME (Multi-Purpose Purpose Extension) is a pure Java implementation of the email application. Allows the user to access, manage and compose email using a web browser. It is POP3 (Post Office Protocol) and SMTP (Exchange Reverse Protocol). It is a multi-protocol web-protocol that contains various features. It is intended to fulfil the need for a stable, fully integrated messaging app in the Java world. This project describes the necessary analysis using a program called Intrinsic Multiple Mail Service that is designed to provide a flexibility to provide users flexibility, networking platforms built that are accessible by the browser.

SMTP, POP, Java script, API, GUI

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M. Arul Pandy, R. Vadivel, "Java Based Intrinsic Multiple Mail Service Application Using Simple Mail Transfer Protocol and Post Office Protocol," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.19-23, 2020.

In this project, we enforce both feature-extraction based totally algorithms and an end-to-cease deep reinforcement mastering technique to discover ways to control Chrome offline dinosaur recreation directly from high-dimensional sport display input. Results display that as compared with the pixel function based totally algorithms, deep reinforcement learning is more effective and effective. It leverages the high-dimensional sensory input immediately and avoids capability errors in characteristic extraction. Finally, we recommend special schooling strategies to address class imbalance issues due to the boom in game velocity. A simple and smooth GUI is supplied for smooth gameplay. The gameplay layout is so simple that user won’t discover it tough to use and understand. Different images are used within the development of this easy recreation project, the gaming environment is similar to the authentic T-Rex Dino Run sport. In order to run the project, you need to have set up python and pygame in your PC. This might be a new word for many however each and every one of us has learned to stroll the usage of the idea of Reinforcement Learning (RL) and this is how our brain still works. A reward gadget is a foundation for any RL algorithm.

Deep reinforcement, Sensory input

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Abujar Shaikh, A.K. Sampath, Anupam Choudhary, "Deep Learning Approach Towards T-Rex Game," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.24-27, 2020.

Project "Online Visa Processing System" is an automated program. It describes the process of applying for a visa. There are many types of visas granted by the government such as H1-visa, Dependent visa. Obtaining a Visa, issuing a Visa is a very meaningful decision, and it is not a contestant. Each visa officer has a list of requirements for which an applicant is applying for a visa. If met, the applicant issues a visa. Otherwise, the applicant does not. It is how these requirements are met that make the visa decision look good. In this paper the proposed system is a web-based system, using one that can store all information in an efficient and efficient way. The system allows storing aggregated data that has the properties to insert, edit and delete data as needed. The program is useful for the organization`s Human Resources department to track staff details and their visits abroad. The program is proposed to consist of the following modules: Study Director, Visa Performance Module, Onsite Communication Module and Reporting Module.

Visa Preparation, Administrative Management System, Decision Making, Web Use, Graphical User Interface

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M. Saravanan, R. Vadivel, "Automated Visa Dispensation Application System to Maintain a Repository of Information about Visa Applicants," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.28-32, 2020.

Network is a node collection. The network`s basic aim is to transfer information from one location to another. This information must obviously be secured from access by third parties. The cryptography concept was based upon the necessity to secure critical data exchanged across an unsecured network. While using encryption the transmitter encrypts or encodes the information with a secret key so that only the tender recipient will understand it. Cryptanalysis, however, means unwanted access without the secret information key. The cryptography uses various techniques such as Diffie Hellman, AES, RSA, DES, IDEA, BLOWFISH, x.509, PKI, digital signatures to convert plain texts into the respective chipper text. In different circumstances all these algorithms are important. RSA`s most productive computerized signature calculation. This article presents a precise writing review of different computerized signature frameworks dependent on RSA. A basic report is completed on the key age, the creation of marks and the mark check of different computerized signature approaches.

Digital Signature, RSA, Cryptography, Key Generation, signature creation, signature verification

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Namrata Vijay, Kaptan Singh, Amit saxena, "A Systematic Literature Review of Various Digital Signature Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.33-37, 2020.

Criminal records usually contain personal information about a particular person and image. To identify any criminal, require an identity document in person, provided by eyewitnesses. In many cases the quality and resolution of parts of the recorded images is poor and difficult to detect. Identification can be done in many ways such as finger print, eyes, DNA etc. One of the programs is facial recognition. Although the ability to use intelligence or character in suspicious facial expressions, one`s ability to recognize faces is amazing. Criminal records usually contain personal information about a particular person and image. The identification of any criminal requires specific identification in relation to a particular person or persons, provided by eyewitnesses. Based on the information provided by eyewitnesses, this investigation will be conducted. In many cases the quality and resolution of parts of the recorded images is poor and difficult to detect. In this paper, it is divided into the performance of graphical images in three stages; low, medium and high level to process and analyze a given face. This paper demonstrates better results than the conventional methods associated with the face recognition process used in crime detection.

Biometrics, Face recognition, Digitization, Preprocessing, Restoration, Compression

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[8] T. Ramesh and S.Sumithra, “A Review on Different Approaches of Mutation Cost Reduction Techniques”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, Issue.1, pp.771-775, January 2017.
[9] Cai, D., He, X., Han, J. and Zhang, H.J, “Orthogonal laplacianfaces for face recognition”. IEEE transactions on image processing, Vol.15, Issue.11, pp.3608-3614. 2006.
[10] H. Fatemi, H. E. Malek, R. Kleihorst, H. Corporaal, and P. Jonker, “Real-time face recognition on a mixed SIMD VLIW architecture,” 4th seminar on embedded systems, Proceedings, (NieuUsergein, The Netherlands), 22 October 2008.
[11] S.Anila&Dr.N.Devarajan, “Preprocessing Technique for Face Recognition Applications under Varying Illumination Conditions”, Global Journal of Computer Science and Technology Graphics & Vision, pp.13-18, 2012.
[12] Boya Akhila1 , Burgubai Jyothi, “Face Identification through Learned Image High Feature Video Frame Works”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.24-29, August 2018.
[13] Ratnesh Kumar Shukla1, Ajay Agarwal , Anil Kumar Malviya, “An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43 , June 2018.

V. Jebasheeli, R. Vadivel, "Implementation of Automated Criminal Face Detection System Using Facial Recognition Approach," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.38-42, 2020.

In the state of affairs of Digital Security there has been an alternate from the vicinity of Digital Guiltiness to the part of Digital War in the route of the most present day couple of years. As indicated by the new difficulties, the master network has two principle draws near to embrace the way of thinking and techniques for Military Insight, and to utilize Man-made brainpower strategies for balance of Digital Assaults. This paper portrays a portion of the outcomes got at Specialized the American College of Sofia in the usage of undertaking identified with the use of insightful techniques for expanding the security in PC systems. The investigation of the achieve ability of different Man-made reasoning strategies has demonstrated that a technique that is similarly successful for all phases of the Digital Knowledge can`t be distinguished. While for Strategic Digital Dangers Knowledge has been chosen and examined a Multi-Specialist Framework, the Repetitive Neural Systems are presented for the necessities of Operational Cyber Threats Artificial Intelligence.

Remote Network Monitoring, Artificial Intelligence, Sequential Feature Selection, Behavioral Assessment, Cyber Threats Intelligence Neural Networks

[1] Elike Hodo, Xavier Bellekens, Ephraim Iorkyase, Andrew Hamilton, Christos Tachtatzis and Robert Atkinson. Machine Learning Approach for Detection of nonTor Traffic. Journal of Cyber Security, Vol. 6.2, pp 171–194, November 2017. doi: 10.13052/jcsm2245- 1439.624
[2] [RFC 1757] Remote Network Monitoring Management Information Base, Carnegie Mellon University, 1995
[3] Brian P. Kime Threat Intelligence: Planning and Direction, SANS Institute, 2015
[4] Hammon P.S. and Sa V.R. de, Preprocessing and meta- classification for brain-computer interfaces, IEEE Transactions on Biomedical Engineering, 54(3), 2007.
[5] Koprinkova-Hristova P., Bozhkov L. and Georgieva P., Echo State Networks for feature selection in affectivecomputing
[6] Republic of Bulgaria: National Cyber Security Strategy “Cyber Resilient Bulgaria 2020”, 2016-03 NCSS Bulgaria final draft v 5 3, Bulgarian government, 2016
[7] Cyber Intelligence: Setting the Landscape for an emerging Discipline, Intelligence and National Security Alliance, INSA, 2011
[8] Operational Level of Cyber Intelligence, INSA, 2013
[9] Operational Cyber Intelligence, INSA, 2014
[10] Liu Y., Sourina O. and Nguyen M. K., Real-time EEG-based human emotion recognition and visualization, in Proceedings of the International Conference on Cyberworlds (CW ’10), 2010, Singapore
[11] L. Bozhkov, P. Georgieva Classification models of emotional bio signals evoked while viewing affective pictures
[12] Erik Hjelmvik, Passive Network Security Analysis with Network Miner, (IN)SECURE Magazine, no. 18, pp. 18–21,2008.
[13] Lukosevicius M. and Jaeger H., Reservoir computing approaches to recurrent neural network training, Computer Science Review, vol. 3, 2009
[14] ENISA Threats Landscape Report 2016: 15 Top Cyber-Threats and Trends, ENISA, 2017
[15] Guyon I. and Elisseeff A. An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol. 3, 2003

Mangoldip Saha, Anirbit Sengupta, Abhijit Das, "Cyber Threats in Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.43-47, 2020.

In this paper, it is been studied the robustness of light traffic obtained by the tuning algorithm for various traffic networks. Since processing the programming algorithm is unaffected using the standard mathematical tool, the aim is to reduce the maximum likelihood of saturation obtained by the algorithm. In large deviation settings, this problem is equivalent to increasing the amount of asymptotic decomposition of the maximum possible. It is start by getting more of the decaying level of the fullness of the line as the line`s fullness decays closer to infinity. Subsequently, made study several structural properties of the low-cost method of linear saturation in a large length, which is an approximation to the decay rate of the largest saturation point. Given these properties, proven that the line with the largest length follows the sample path that extends through the line. At a certain parameter value, the sorting algorithm is very good at reducing the length of the largest line. For numerical results, it is have shown the large-scale deviation properties of the line lengths that are commonly used while varying one algorithm parameter.

Line saturation, wireless algorithms, Round Robin programming, Scheduling greed

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[8] T. Ramesh and S.Sumithra, “A Review on Different Approaches of Mutation Cost Reduction Techniques”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, Issue.1, pp.771-775, January 2017.
[9] D. Shah and D. Wischik, “Optimal scheduling algorithms for inputqueued switches,” in Proceedings IEEE INFOCOM, Barcelona, Spain, Apr. 2006.
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[12] M. J. Neely, “Delay analysis for maximal scheduling in wireless networks with bursty traffic,” in Proceedings IEEE INFOCOM, Phoenix, AZ, April pp. 6–10. 2008.
[13] R. Kumari1 , P. Nand, “Performance Analysis of Existing Routing Protocols”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.47-50, October 2017.
[14] S.D.N. Hayath Ali , M. Giri, “A Study on Current Challenging Issues and Optimal Methods for Video Streaming over Heterogenous Wireless Network”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, April 2018.

S. Vineeth, R. Vadivel, "Automatic on-Demand Selection of Suitable Wireless Scheduling Algorithm to Minimize Queue Overflow Probability," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.48-53, 2020.

Humans are called as social animals and because of that communication becomes a very integral part of a human being. Humans use verbal and non-verbal forms of speech for communication purposes, but not all humans are capable of verbal speech, for e.g. Deaf and Mute people. Hence, Sign Languages are developed for them, but still there is a hindrance in the communication for them. So, using the hand gestures, this paper presents a system where CNN network is used to for the classification of Alphabets and Numbers. CNN is used because alphabets and number gestures are static gestures in Indian Sign Language and CNNs give very good results for image classification. This uses hand-masked (skin-segmentation) images for training the model. For the dynamic hand gestures, the system uses LSTM network for the classification task. LSTM are well known for accurately predicting the data which is distributed in time-frame. This paper presents two models, CNN and LSTM for predicting different type of hand gestures i.e. static as well as dynamic.

Indian Sign Language, CNN, Skin-segmentation, LSTM

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[6] A. S. Nikam, A. G. Ambekar, "Sign Language Recognition Using Image Based Hand Gesture Recognition Techniques", In the Proceedings of the Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, pp.1-5, 2016.
[7] N. S. Lele, “Image Classification Using Convolutional Neural Network”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol.6, Issue.3, pp.22-26, 2018.
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[11] L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D Lin, X. Tang, and L. Van Gool, "Temporal Segment Networks: Towards Good Practices for Deep Action Recognition", In the Proceedings of the European conference on Computer Vision, pp.20-36. Springer, Cham, 2016.
[12] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, “Large-scale Video Classification with Convolutional Neural Networks”, In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1725-1732, 2014.
[13] J. Sun, J. Wang, T. C. Yeh, “Video understanding: from video classification to captioning”. In the Proceedings of the Computer Vision and Pattern Recognition, pp.1-9, Stanford University, 2017.
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[15] Pradip Patel, Narendra Patel, “Vision Based Real-time Recognition of Hand Gestures for Indian Sign Language using Histogram of Oriented Gradients Features”, in International Journal of Next-Generation Computing, Vol. 10, No. 2, July 2019.

Manav Prajapati, Mitesh Makawana, Sahil Hada, "Indian Sign Language Recognition for Static and Dynamic Hand Gestures," International Journal of Computer Sciences and Engineering, Vol.8, Issue.9, pp.54-58, 2020.