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Visual cryptography is a technique to diffuse and disguise a secret image into a 2D pattern of black and white pixels, called shares. Individual or any k-1 shares have no clue about the secret image, but any k shares out of n, can decode the secret image. These meaningless shares are subject to suspect for the intruders. Stacking of shares reconstructs the secret image populated with noisy pixels. In addition to these traditional visual cryptography suffer with pixel alignment problem. To resolve these problems this paper proposes a (2, 2) mutated random grid approach to share a secret image into visually pleasing shares. The solution to the problem is based on assignment of instances of random two dimensional matrix of binary numbers and its XOR operation with respective pixels with auxiliary gray image. It requires lightweight computing device to decode the secret image. Comparative analysis of the experimental results with that of existing fundamental approaches shows that proposed approach performs better.

Visually Pleasing Shares, Mutated Random grids, XOR, Visual Secret Sharing, Light weight Computation, Pixel Expansion, Contrast-loss

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[22] Wu, X., Ou, D., Dai, L., and Sun, W., “Xor-based meaningful visual secret sharing by generalized random grids”, In Proceedings of the first ACM workshop on Information hiding and multimedia security, pp. 181-190, 2013
[23] Pang L, Miao D, Lian C., “Userfriendly randomgridbased visual secret sharing for generalaccess structures” Security and Communication Networks, Vol.9, Issue.10, pp.966-976, 2016 Jul 10
[24] S.B. Bhagate, P.J. Kulkarni, "Construction of Basis Matrices for (k, n) and Progressive Visual Cryptography Schemes", International Journal of Computer Sciences and Engineering, Vol.06, Special Issue.01, pp.43-47, 2018
[25] Komal S. Patil, Suhas B. Bhagate, "Progressive Visual Secret Sharing Scheme for QR Code Message", International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.882-887, 2019.

Jasvant Kumar, Suresh Prasad Kannojia, "Mutated Random grid Approach to Share Secret Image into Visually Pleasing Shares," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.1-6, 2021.

The method of identifying the disease with person’s symptoms and signs is medical diagnosis. Brain tumour is the stimulating disorder that has to be identified at early stage for treatment. Many classification techniques have been introduced for performing brain tumour identification. However, the brain tumour identification accuracy level was not enhanced and time consumption was not lessened. In order to address these problems, Projection Pursuit Feature Selective Bivariate Multilayer Perceptred Classification (PPFSBMPC) Method is introduced. PPFSBMPC Method comprises two processes, namely feature selection and classification for brain tumour identification. To select the relevant features from the input database, Projection Pursuit Feature Selection process is carried out in PPFSBMPC Method. After performing the feature selection, Bivariate Multilayer Perceptred Classification process is accomplished for brain tumor identification. In addition, the classification process comprised multiple layers to categorize the input data as normal data or tumour diseased data. By this way, PPFSBMPC Method increases the brain tumor identification performance with higher accuracy and lesser time consumption. Experimental evaluation of PPFSBMPC Method is carried out with Epileptic Seizure Recognition Dataset on factors such as brain tumour identification accuracy, execution time, and error rate with respect to number of patient data. The experimental result demonstrates that the PPFSBMPC Method enhances the brain tumour identification accuracy and reduces the execution time when compared to state-of-the-art-works.

Medical diagnosis, brain tumour, classification, feature selection, classification process, identification, seizure

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[17] Diah P. Wulandari, Nomala G. P. Putriz, Yoyon K. Suprapto and Santi W. Purnami, Anda I. Juniani and Wardah R. Islamiyah, “Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals”, Procedia Computer Science, Elsevier, Volume 161, Pages 568-576, 2019.
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Renjeni P.S., B. Mukunthan, "Identification of Brain Tumor Using Projection Pursuit Bivariate Multilayer Perceptred Classification," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.7-14, 2021.

In today’s generation, authentication is one of the biggest problems in our society. So, one of the most known techniques used for authentication is human face recognition, which is also known as HFR. For example- nowadays we can unlock our phone using the face recognition feature. In the existing system, Our lecturers take attendance manually which is somewhat time-consuming and old school type. So, our Artificial Intelligence-based attendance monitoring system will be capturing the faces of every student in a class during attendance and the result will get stored in the database automatically. There will be no extra Radio frequency Identification card, people need to carry anymore and this system will be the most authentic system of taking attendance. The system stores the faces that are detected and automatically uploads the attendance to the database. Using This process our primary goal is to help lecturers as well as students to track and manage student`s attendance and absenteeism.

Artificial Intelligence, Face Recognition, Attendance Monitoring System, Database, Smart Attendance Monitoring System, Facial Recognition Based Automated Attendance System, Chatbots

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[6] Kavita, M. Kaur, “A Survey paper for Face Recognition Technologies”, International Journal of Scientific and Research Publications, Vol. 6, Issue 7, July 2016.
[7] Y.S.V. Lakshmi, V.J. Kumar, International Journal of Computer Sciences and Engineering, “Smart Biometric Attendance and Monitoring System”, International Journal of Computer Sciences and Engineering, Vol. 7, Issue 6, June 2019.
[8] S.Patel, P. Kumar, S. Garg , R. Kumar, “Face Recognition based smart attendance system using IOT”,International Journal of Computer Sciences and Engineering, Vol.6, Issue 5, pp.871-877, May 2018.
[9] Md. Abdur Rahim, Md. Najmul Hossain, T. Wahid & Md. S. Azam, “ Face Recognition using Local Binary Patterns”, Global Journal of Computer Science and Technology Graphics & Vision, Volume 13, Issue 4 , Version 1.0, 2013.
[10] A.Patil, Priya K.P, P. More, A. Joshi, A.R. Kamble,“Attendance Monitoring using Face Recognition and Machine Learning”,International Journal of Future Generation Communication and Networking, Vol. 13,No. 3s,pp. 94–102, 2020.
[11] S.Babu, R.R. Rao, Shruddha, S. Lahari, V.V. Shetty, “AN EXPLORATORY STUDY ON FACE RECOGNITION BASED ATTENDANCE MONITORING SYSTEM”,International Journal of Creative Research Thoughts (IJCRT), Volume 9, Issue 2, February 2021.
[12] M.Dahiya,”A Tool of Conversation: Chatbot”,International Journal of Computer Sciences and Engineering, Volume-5, Issue-5, pp.158-161, 2017

Ankur Hati, Sagar Saha, Ankita Mandal, Preeti Saha, Sudipta Sahana, Dharampal Singh, "Face Recognition & AI Based Smart Attendance Monitoring System," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.15-21, 2021.

With the increasing popularity of the internet as well as the evolving acceptance of cashless policy, information and online transaction processing systems are generally more susceptible to direct attack and abuse than their offline counterparts. Various security techniques have previously been developed to regularly assess the vulnerability of these systems and provide security to users. However, a number of these security techniques have proved to have bottlenecks thereby, putting sensitive financial information, services and products at risk of cyber-attacks. In this work, an enhanced security model that improves the security of the online transaction processing system is designed. This algorithm combines the features of Multilevel Security (MLS) and the Bell-Lapadulla model (BLM) to ensure the secure state of the system. Additionally, Mandatory Access Control (MAC) mechanism was used to enhance the security of the sensitive information/data shared during the online transaction processing. The methodology adopted was Object Oriented Hypermedia and Design Methodology (OOHDM) which is well suited for analyzing and designing objects that make up the new security enhanced system. Microsoft Visual Studio 2010 was used as our development environment. The programming language used was PHP and Java Script, while MySQL Server 2008 was used in the development of the database engine. Enhancing the security requirement(s) of the system was considered. The results showed that the enhanced security model using Mandatory Access Control (MAC) mechanism offered a highly secured system where users and organizations felt protected while carrying out transactions online.

MAC, OOHDM, MLS, BLM

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Allwell Ononiwu Akanwa, Virginia. E. Ejiofor, "Enhanced Security Model for Information and Online Transaction Processing System Using Mandatory Access Control (MAC) Mechanism," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.22-30, 2021.

The recognition of image is one of the most important fields in the image processing and computer vision. Image recognition has many branches but the food image classification is very unique. In today’s world people are very conscious about their health. Many people around the world use some dietary assessment system for planning of their diet. In dietary assessment system people make the use of food image classification to classify the food from the image and provide the total amount of calories present in the food. The classification of food images is a very difficult task as the dataset of food images is highly non-linear. In this paper, we are going to use different types of neural network models to show, which neural network provides the best accuracy result in the recognition of food images and is most efficient to use. We are using a food image dataset (food-11) which contains 16643 images in it.

Deep Learning, CNN, RNN, Computer Vision, Image processing, DCNN

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Yash Baid, Avinash Dhole, "Food Image Classification Using Machine Learning Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.31-36, 2021.

A tumour is an irregular mass of cells and it can either be benign (non-cancerous) or malignant (cancerous). Disease alludes to cells that outgrow control and attack different tissues. One of the reasons for malignancy passing in person is Lung Cancer. Clinical therapy with drugs intended to target lungs disease cell to diminish the spread all through the body may likewise conceivable yet before this it is must to perceive the malignant growth at the beginning phase. Physically disease recognizable proof is tad of tedious so that with the progression of innovation, Several Computer Aided Diagnosis (CAD) frameworks are created for distinguishing cellular breakdown in the lungs in its beginning phase. In this paper inclination in detail literature survey on various techniques that have been used in feature extraction and classification with its obtain accuracy.

CAD, SIFT, SVM, ANN

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Astha Pathak, Avinash Dhole, "Image classification Method in detecting Lungs Cancer using CT images: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.37-42, 2021.

The detection of a health problem, illness, disability, or other condition that an individual may have is known as disease diagnosis. Large data sets are available; however, the tools that can accurately evaluate trends and make predictions are limited. Traditional methods of diagnosing diseases are considered to be not effective in getting accuracy and prone to error. Artificial Intelligence (AI) is being used to forecast the future. AI with predictive techniques enables to provide auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper we have taken review of sepsis detection in newborn infants using techniques of AI, like Fuzzy Logic and identified limitations of these studies. The aim of this research paper is to reveal some key insights into medical techniques. Based on a series of open problems and challenges, the paper also suggests some directions for potential research on AI-based diagnostics systems.

Disease, diagnosis, Sepsis Detection, Fuzzy logic

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M.S. Kalas, Nikita D. Deshpande, "Sepsis Detection in newborn infants - Diagnosis using fuzzy inference system- A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.43-46, 2021.

Privacy protection as a process of collection, processing and dissemination of Information is a burgeoning issue that is challenging researchers, scientists and regulators. A universal ethical concern of the organizations, individuals and society at large is how the Information is accessed and manipulated. Technology creates implications for the privacy of people in a variety of areas, and privacy Issues appear at a variety of platforms calling for the formation of standards and regulations. The regulations are still evolving and are not optimal since the technological contexts are highly dynamic. Our paper presents the privacy trends, regulation of privacy in some standard frameworks, technological contexts and emerging ethical considerations. We also highlight the ethical issues with privacy protection and motivates to develop new methodologies to handle privacy from a both legal and technological perspective. The technological context and legal framework go together for the protection of privacy. However, privacy issues are complex and will continue to evolve. People have to find the best ways of handling ethical issues in specific situations. Researches on the protection of privacy in various computing environments may be carried out given a specific technology environment that can in customizing for region-specific legislation.

Privacy, Regulation, Standards, Data Security, GDPR, Technological Responses

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Ayush Gupta, Manvi Gupta, "Privacy Protection - Emerging Issues and Technological Responses," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.47-54, 2021.

AIX regression test suite execution will take place on every weekly build, and this requires one or more lpars with pre-defined configurations need to be dedicated per job( eg: for specific function/feature/area). and each lpar needs a NIM installation.This model has a limitation of most of the H/W remains unutilized till next execution starts (due to dedicated H/W per job) and reconfigurations to improve utilization involves admin efforts.To address this limitation, we are going with new approach where Lpar/VMs will be created with required config on demand when there is a requirement, and it will be recycled (Memory, CPU, and Disks) immediately once the work is done in automated way with the help of PowerVC

VMs. Mem, Proc, storage, PowerVC, HMC, LPAR, FSP

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Pavan G Yajurvedi, Vishwanath R. Hulipalled, "Resource Utilization Using PowerVC Based Auto-Provisioning," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.55-60, 2021.