Volume-8 , Issue-7 , Jul - 2020 Go Back

It is obvious that the World right now is in the stagnant position due to COVID-19. The problem of this pandemic is its mode of transmission from person to person on daily basis. This study aims at modelling the spreading pattern of the disease in Nigeria with a view to understanding how the spread can be curbed. Secondary data collected from Nigerian Center for Disease Control (NCDC) between 29th Feb 2020 and 30th May 2020 were used. These daily data were further grouped into 14 weekly data. Parameters like number of suspected persons, the number of people quarantined and the total number of active cases were used to develop the model thus; the undetected infected people were termed as Hidden nodes (d) defined as d_i=x_i-q_i , where: x_i is the suspected case of period i and q_iis the number of infected people that were isolated in period i. Each undetected infected person d (hidden node) can infect t several people in a given time (day, week or month), also those infected that show symptoms will also be fished out, tested and isolated if they were positive will be recorded as q. This model has established the maximum number of days to be spent in a lockdown period, given a certain number of confirmed cases to control the spread of the disease

Modelling, Hidden nodes, COVID-19, NCDC, pandemic, coronavirus

[1] Adam J Kucharski, Timothy W Russell, Charlie Diamond, Yang Liu, John Edmunds, Sebastian Funk, Rosalind M Eggo (2020), ?Early dynamics of transmission and control of COVID-19: a mathematical modelling study? Lancet Infectious Disease 2020; 20: 553?58, https://doi.org/10.1016/, Published Online March 11, 2020.
[2] Manav R. Bhatnagar (2020) ?COVID-19: Mathematical Modeling and Predictions? Research Gate, DOI: 10.13140/RG.2.2.29541.96488, Published on April 2020.
[3] Yichi Li1, Bowen Wang, Ruiyang Peng, Chen Zhou, Yonglong Zhan, Zhuoxun Liu, Xia Jiang and Bin Zhao ?Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures? Annals of Infectious Disease and Epidemiology 1 2020, 5(1), Article 1052, 2020.
[4] Manav R. Bhatnagar (2020) ?A Statistical Model for The Spread of Covid19 in Clusters,? Research Gate, DOI: 10.13140/RG.2.2.18583.52644, Published on April 2020.
[5] Rauf Rauf Ibrahim, Hannah Oluwakemi Oladipo (2020) ?Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure?, Doi :https://doi.org/10.1101/2020.05.05.20091686. Published online May 8, 2020.
[6] Arti M.K., Kushagra Bhatnagar (2020) ?Modeling and Predictions for COVID 19 Spread in India?, Research Gate, DOI: 10.13140/RG.2.2.11427.81444, Published on 02 April 2020.
[7] Zafar Iqbal Khan, Yasir Javed, Khurram Naim Shmasi (2020) ?Correlation study of New Cases, Deaths, Recoveries and Temperature with Machine Learning during COVID-19 spread in Saudi Arabia? International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.3, pp.01-05, June 2020.
[8] Heni Bouhamed (2020) ?Covid-19 Cases and Recovery Previsions with Deep Learning Nested Sequence Prediction Models with Long Short-Term Memory (LSTM) Architecture?, International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.10-15, April 2020.

Abiodun Daniel Olabode, Ibrahim Zakariyya Musa, Shamsuddeen Ahmad Sabo, Muhammad Bello Sambo, "Modelling the Spreading Pattern and Prevention Strategies of COVID-19 in Nigeria," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.1-7, 2020.

This paper outlines the benefits of incorporating the Raspberry PI and Arduino into an undergraduate research course for bachelor`s degree students. In this course, students work in several projects that provide an experience in research and development. In specific it works with electronics components and realized analysis with a variety of instruments. Explanations are made about three small projects implemented during Fall 2019. Our goal and motivation is to provide a hands-on building component and develop of professional and technical skills

Arduino, Raspberry, projects, ms power bi, microcontrollers

[1] S. Monk, "Programming Arduino, Getting Started with Sketches", McGraw-Hill Companies, USA, pp. 20, 2012.
[2] P. Jamieson, "Arduino for Teaching Embedded Systems. Are Computer Scientists and Engineering Educators Missing the Boat?", 2012.
[3] A. Deshpande, A. Wanare, "Design and Implementation of TCP/IP web server on Raspberry Pi", International Journal of Science, Engineering and Technology Research (IJSETR), Vol.4, Issue.12, pp. 4283, 2015.
[4] J. Bayle, "C Programming for Arduino", Packt Publishing, UK, pp. 23, 2013.

L.M. Fern?ndez-G?mez, A. Camacho-Mart?nez, "Electronics Skills With Arduino and Raspberry In Undergraduate Research Course," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.8-10, 2020.

Due to big data and progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete, the exactness of study is reduced. In the proposed system, our system can take either text or image input symptoms from the user and based on the analysis of the symptoms it displays a result. It provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it uses a latent factor model to rebuild the missing data. It experiments on various diseases that occur in human being. Using structured and unstructured data from hospital, Random Forest algorithm is used for classification of text datasets. SSD (Single Shot Multi Box Detector) algorithm is used for image processing to analyse various diseases in human being.

Big Data, Machine Learning, kaggle, CNN

[1] M. Chen, Y .Hao, K. Hwang, L. Wang, and L. Wang, ?Disease prediction by machine learning over big data from healthcare communities?, ,?IEEE Access, vol.5, no. 1, pp. 8869-8879, 2017.
[2] IM. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, and C. Youn,?Wearable 2.0: Enable human-cloud integration in next generation healthcare system,? IEEE Commun. , vol. 55, no. 1,pp. 54?61, Jan. 2017.
[3] P. Sharma, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, V. Yadav, S. Chauhan, Analytical comparison of efficacy for electromyography and galvanic skin resistance biofeedback on audio-visual mode for chronic TTH on various attributes, in Proceedings of the ICCIDA-2018 on 27 and 28th October 2018. CCIS Series (Springer, Gandhi Institute for Technology, Khordha, Bhubaneswar, Odisha, India, 2018)
[4] Shraddha SubhashShirsath ?Disease Prediction Using Machine Learning Over Big Data? International Journal of Innovative Research in Science, Vol. 7, Issue 6, June 2018
[5] AnimeshHazra, Arkomita Mukherjee, Amit Gupta, Mukherjee, ?Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review?, Research Gate Publications, July 2017, pp.2137-2159.
[6] V. Krishnaiah, G. Narsimha, N. Subhash Chandra, ?Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review?, International Journal of Computer Applications, February 2016
[7] AnimeshHazra, Arkomita Mukherjee, Amit Gupta, Asmita Mukherjee, ?Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review?, Research Gate Publications, July 2017, pp.2137-2159
[8] P. Groves, B. Kayyali, D. Knott, and S. V. Kuiken, ?The ?big data? revolution in healthcare: Accelerating value and innovation,? 2016.
[9] S. Patel and H. Patel, ?Survey of data mining techniques used in healthcare domain,? Int. J. of Inform. Sci. and Tech., Vol. 6, pp. 53-60, March 2016.
[10] M. Amiri and G. Armano, "Early diagnosis of heart disease using classification and regression trees," The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, 2013, pp. 1-4. doi: 10.1109/IJCNN.2013.6707080 .
[11] S. Ekız and P. Erdoğmuş, "Comparative study of heart disease classification," 2017 Electric Electronics, Computer Science, Biomedical Engineerings` Meeting (EBBT), Istanbul, 2017, pp. 1-4. doi: 10.1109/EBBT.2017.7956761.
[12] P. Su, J. Yang, Z. Li and Y. Liu, "Mining Actionable Behavioral Rules Based on Decision Tree Classifier," 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, 2017, pp. 139-143. doi: 10.1109/SKG.2017.00030 .
[13] D. Bertsimas, J. Dunn and A. Paschalidis, "Regression and classification using optimal decision trees," 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, 2017, pp. 1-4. doi: 10.1109/URTC.2017.8284195Y

Ajeesh Babu, Fathima Basheer, Jayasanker M, Tintu Mariyam Paul, Sithu Ubaid, "Disease Prediction Using Machine Learning Over Big Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.11-15, 2020.

The main objective of this proposal is to reduce the road accidents due to high speed of moving vehicles in traffic. In the current system the speed limiting boards are monitored by static in nature, due to which on any given day when there is high vehicular traffic moving with high speed there is a need of necessity to decrease the speed of vehicles below the permissible speed shown on the display board. The proposed system introduces the concept of Intelligent Traffic Monitoring System by using ZIGBEE technology along with VANET. According to the system, speed limit boards are erected to the current poles lying on the roads which will display the speed limit. Every area is designated with the set speed limit. When the vehicle passes by that area and there is a speed mismatch of the vehicle with the display board, speed will be recorded and further monitored at the next point and if it is still not decreased then a necessary action will be taken which may also lead to the cancellation of the license of the driver.

ZIGBEE, VANET, TRAFFIC MONITORING

[1] Embedded based vehicle speed control system using wireless technology international journal of innovative research in electrical, electronics, instrumentation and control engineering vol. 2, issue 8, August 2014
[2] Overview of Vanet with its features and security attacks ((irjet) volume: 03 issue: 01 | jan-2016 International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 03 Issue: 01 | Jan-2016 www.irjet.net p-ISSN: 2395-0072 ? 2016, IRJET ISO 9001:2008 Certified Journal Page 137
[3] Zigbee Based Speed Sensing System and Providing Alarm of Over Speed. www.ijareeie.com Vol. 6, Issue 4, April 2017 Copyright to IJAREEIE DOI:10.15662/IJAREEIE.2017.0604036 2427
[4]Vanet based wireless sensor network using zigbee technology(irjet) volume: 05 issue: 05 | may-2018. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 05 | May-2018 www.irjet.net p-ISSN: 2395-0072 ? 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal.
[5] C.K. Toh, ?Ad Hoc Mobile Wireless Networks: Protocols and Systems?, Prentice Hall publishers, December 2001, ISBN 0130078174.
[6] P. Papadimitratos and Z. Haas."Secure routing for mobile ad hocnetworks" (SRP) SCS Communication Networks and Distributed Systems Modeling and Simulation Conference, pp. 27--31, January 2002.

Y.S. Vijaya Lakshmi, V. Jagadeesh Kumar, R. Kanaka Raju, "Intelligent Traffic Monitoring System by Using ZIGBEE and VANET Technology," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.16-18, 2020.

The term big data arose under the explosive increase of global data as a technology that is able to store and process big and varied volumes of data, providing both enterprises and science with deep insights over its clients? experiments. Big Data provides a reliable, fault-tolerant, available and scalable environment to harbor big data distributed management systems thus provide a need store our data at cloud providers place utilizing cloud computing technology. Attribute Based Encryption (ABE) techniques came into existence for securing and providing access control with its many attendant problem, more so with the use of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Key-Policy Attribute-Based Encryption (KP-ABE). Big Data is used to maintains and manage valuable data that are store in the cloud. Having the cloud itself not fully trusted possess a lot of issues thereby making the big data in the cloud to face many threats that are not disclosed by services providers. For these reasons we are proposing an authentication protocol for big data with hierarchical attribute-based encryption and authorization structure which will provides a secure authentication protocol for two-level hierarchical attribute-based encryption and authorization structure of cloud big data access control system that will authenticate authorities or users. Our proposed protocol resorts to tree-based signature to significantly improve the security of attribute authorization thus providing data owner a level of security on the data that will require at least two-level of attributes been satisfy before big data can be access. To satisfy big data requirements, we proposed authentication protocol that support two levels of hierarchical attribute-based encryption and authorization structure using a combination of advanced encryption standard (AES), elliptic curve cryptography combining with the hardness of Diffie Hellman theorem (ECDH). Often times, data and file access control encryption were usually implemented with RSA and DSA protocol, which also comes with their own attendant problems, such as computational overhead cost, time sequence for both encryption and decryption key, encryption keys bit length which also culminate in longer period of time for process execution, We proposed a protocol for authentication and key exchange using AES (Advance Encryption Standard) and ECDH (Elliptic Curve Diffie Hellman) that help to resist forgery attack, replay attack, short key bits length thereby enable less utilization of bandwidth and availability on both mobile and desktop computing with robust security based on hardness of Diffie Hellman and Elliptic Curve cryptography algorithm. In addition, we proposed protocol that help preserve entities privacy, our protocol performance is far better than existing protocol, ours enable less power consumption and low bandwidth consumption as its key length invariably has lower bits than other protocol bits lengths. Comparing with the previous studies, we proof and show that our protocol has lower computational and communication overhead. We propose an authentication protocol for big data with the hierarchical attribute authorization structure which require that a trusted root authority grant access to data owner or domain authority that must define data users? attributes and each of the attributes are structure in terms by domain authority. For security of data we use ECDH and AES scheme. Elliptic curve ciphers require less computational power, memory and communication bandwidth giving it a clear edge over a traditional crypto-algorithms. Lately, many companies have adopted the use of ECDH algorithm to improve security efficiency (WhatsApp, Facebook, Firefox, etc.). In hierarchical attribute authorization adopted two levels - Trusted Root Authority (TRA) and Domain Authority (DA). The TRA acts as an authorization manager and DA?s in second tier as subordinated to the TRA.

Big Data, Cloud Computing, Authentication Protocol, Hierarchical Attribute Set Based Encryption, Ciphertext Policy Attribute Base Encryption, Elliptic Curve Cryptography, Diffie Hellman, ECDH, AES, ABE, CP-ABE, RSA, TRA

[1] Toninelli, A.; Montanari, R.; Kagal, L.; Lassila, O. A semantic context-aware access control framework for secure collaborations in pervasive computing environments. In Proceedings of the International Semantic Web Conference, Athens, GA, USA, 5?9 November 2006; pp. 473?486.
[2] Botta, A.; De Donato, W.; Persico, V. Pescap?, A. Integration of cloud computing and internet of things: A survey. Future Gener. Comput. Syst. 2016, 56, 684?700.
[3] Zissis, D.; Lekkas, D. Addressing cloud computing security issues. Future General. Computer. Systems. 2012, 28, 583?592.
[4] Bouabana-Tebibel, T.; Kaci, A. Parallel search over encrypted data under attribute-based encryption on the Cloud Computing. Comput. Secur. 2015, 54, 77?91.
[5] Akl, S.G.; Taylor, P.D. Cryptographic solution to a problem of access control in a hierarchy. ACM Transmission. Computer. Syst. 1983, 1, 239?248.
[6] Castiglione, A.; De Santis, A.; Masucci, B.; Palmieri, F.; Huang, X.; Castiglione, A. Supporting dynamic updates in storage clouds with the Akl?Taylor scheme. Inf. Sci. 2017, 387, 56?74.
[7] Akl, S.G.; Taylor, P.D. Cryptographic solution to a problem of access control in a hierarchy. ACM Transmission. Computer. Syst. 1983, 1, 239?248.
[8] Crampton, J.; Farley, N.; Gutin, G.; Jones, M.; Poettering, B. Cryptographic enforcement of information flow policies without public information via tree partitions 1. J. Computer. Security. 2017, 25, 511?535.
[9] Goyal, V.; Pandey, O.; Sahai, A.; Waters, B. Attribute-based encryption for fine-grained access control of encrypted data. In Proceedings of the 13th ACM Conference on Computer and Communications Security, Alexandria, VA, USA, 30 October?3 November 2006; pp. 89?98.
[10] Bethencourt, J.; Sahai, A.; Waters, B. Ciphertext-policy attribute-based encryption. In Proceedings of the IEEE Symposium on Security and Privacy (SP?07), Berkeley, CA, USA, 20?23 May 2007; pp. 321?334.
[11] Waters, B. Ciphertext-policy attribute-based encryption: An expressive, efficient, and provably secure realization. In Proceedings of the International Workshop on Public Key Cryptography, Taormina, Italy, 6?9 March 2011; pp. 53?70.
[12] Bethencourt, J.; Sahai, A.; Waters, B. Ciphertext-policy attribute-based encryption. In Proceedings of the IEEE Symposium on Security and Privacy (SP?07), Berkeley, CA, USA, 20?23 May 2007; pp. 321?334.
[13] Lai, J.; Deng, R.H.; Li, Y. Expressive CP-ABE with partially hidden access structures. In Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security, Seoul, Korea, 2?4 May 2012; pp. 18?19.
[14] Waters, B. Ciphertext-policy attribute-based encryption: An expressive, efficient, and provably secure realization. In Proceedings of the International Workshop on Public Key Cryptography, Taormina, Italy, 6?9 March 2011; pp. 53?70.
[15] Lee, C.-C.; Chung, P.-S.; Hwang, M.-S. A Survey on Attribute-based Encryption Schemes of Access Control in Cloud Environments. IJ Netw. Secur. 2013, 15, 231?240.
[16] Li, Y.; Zhu, J.; Wang, X.; Chai, Y.; Shao, S. Optimized ciphertext-policy attribute-based encryption with efficient revocation. Int. J. Security. Its Appl. 2013, 7, 385?394.
[17] Hongbing, C., R Chunrning, H Kai, W. Weihong and L. Yanyan: Secure big data storage and sharing scheme for cloud tenants. China Communication., 12: 106-115, 2015.
[18] Sookhak, M., A Gani, M.K. Khan and R Buyya: Dynamic remote data auditing for securing big data storage in cloud computing. Inf Sci., 380:101-116.2017.
[19] Puthal, D., S. Nepal, R Ranjan and J. Chen: DPBSV-an efficient and secure scheme for big sensing data stream. Proceedings of the 2015 IEEE Conference on Trustcom/BigDataSE/ISPA Vol. 1, August 20-22, 2015, IEEE, Helsinki, Finland, ISBN:978-1-4673-7951-9, pp: 246-253, 2015.

S.E. Tuase, D. Matthias, N.D. Nwiabu, "Big Data Authentication Protocol with Hierarchical Attribute-Based Encryption and Authorization Structure," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.19-31, 2020.

Cryptography and Steganography are two favourite techniques used by developers for security reasons.Cryptography is the study of information hiding and verification. It includes the protocols,algorithms and strategies to securely and consistently prevent or delay unauthorized access to sensitive information and enable verifiability of every component in a communicate.Steganography is the process of hiding text in an image.we propose an encrypting system which combines techniques of cryptography and steganography with data hiding which is highly useful in secret keeping areas. Image steganography and cryptography using three level security is used for transferring text message from sender to receiver.The main aim of project is to provide the user a secure way that helps the user to send and receive secret messages.There is three level authentication system that validates user for accessing the system only they have input correct one time password.The project involves three levels of authentications for the sender and receiver. In this sender will have to first go through all the three stages of authentication.After going through all the stages the senders text will be encrypted using the cryptography algorithm.These encrypted text will be hidden inside the image.After getting this image he can be send this image and verify OTP to receiver?s mobile number.The receiver also has to through all the stages of authentication and use one time password for verification and decrypt the image and get the message hidden inside the image

Cryptography,Steganography,Security,OTP(OneTimePassword)

[1] Rupesh Gupta , Dr .Tanu Preet Singh?New Proposed Practice for Secure Image Combing Cryptography Steganography and Watermarking based on Various Parameters? International Conference on Contemporary Computing and Informatics (IC3I), 2014.
[2] Mamta Juneja, Parvinder Singh Sandhu?Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption? International Conference on Advances in Recent Technologies in Communication and Computing, 2009.
[3] International Journal of Applied Information Systems ?Efficient Data Hiding System using Cryptographyand Steganography?.
[4] ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, FEBRUARY ?Pixel pattern based steganography on images?, 2015.
[5] S.Lyu and H. Farid, ?Steganography using higher order image statistics, ?IEEE Trans. Inf. Forens. Secure, 2006.
"Advanced Encryption Standard "(AES), National Institute of Standards and Technology (NIST), U.S. FIPS PUB 197 (FIPS 197), 2001
[6] T.Morke1, "An Overview of Image Steganography", Department of Computer Science, University of Pretoria, South Africa
[7] Piyush Marwaha, Paresh Marwaha,"Visual Cryptographic Steganography in Images", Infosys Technologies Limited, India
[8] William Stallings, "Cryptography and Network Security: Principles and Practices", Third edition, Chapter 2 and Chapter 5.
[9] James Lyons `The Playfair Cipher.". Practical Cryptography I, July 2009.
[10] Swati Nimje, Amruta Belkhede, Gaurav Chaudhari, Akanksha Pawar and Kunali Kharbikar,"Hiding Existence of Communication Using Image Steganography " in International Journal of Computer Science and Engine and Engineering(IJCSE), Volume-2, Issue-3 ,E-ISSN: 2347-2693.Mar-2014.
[11] Unik Lokhande, A.K.Gulve Steganography using Cryptography and Pseudo Random Numbers? in an International Journal of Computer Applications (0975 ? 8887) Volume 96? No.19, June 2014.
[12] M. S. Sutaone,M.V. Khandare,"Image Based Steganography Using LSB Insertion Technique" in IET International Conference on Wireless, Mobile and Multimedia Networks, 2008.
[13] Arati Appaso Pujari,Sunita Sunil Shinde,"Data Security using Cryptography and Steganography" in IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 4, Ver. V (Jul.-Aug. 2016), PP 130-139.
[14] Pooja Rani, Mrs. Preeti Sharma,"Cryptography Using Image Steganography" in an International Journal of Computer Science and Mobile Computing, Vol.5 Issue.7, pg. 451-456, July- 2016.
[15] Miftah Ul Uroos, Sukhvinder Kaur ,Muheet Ahmed Butt , Steganography: A Comparative Survey Conducted on Digital Images? in IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 10 (October.), ||V (I) || PP 52-61, 2018.

Jeena Thomas, Jeena M. Roy, Malu M., Vrinda Vijayan, Anoop S., "Image Steganography And Cryptography Using Three Level Password Security," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.32-35, 2020.

This paper proposes the concept of Machine Learning to implement an eye blink detection system. It is used to first track the eyes on the patient and then detect its movements. Machine learning has experienced a boost in acceptance among many fields including the medical field. People suffering from speech impairment find it difficult to communicate their needs to the outside world. People with severe disabilities are confined in a state in which communication is virtually impossible, being reduced to communicating with their eyes or using sophisticated systems that translate thoughts into words. The Eye Trackers are suitable systems for those people but the main disadvantage is the cost. More affordable devices are capable of detecting voluntary blinks and translating them into a binary signal that allows the selection. The method of voluntary blinking, the use of long or double blinks had no statistical influence on accuracy, excluding EOG, and the time taken to perform double blinks was shorter, resulting in a potentially much faster interface. Machine learning creates a platform to be precise in the measurement of any parameters using various algorithms. In this paper, we propose to apply Haar cascade and shape predictor algorithms to map the eyes of the patient and detect various blink patterns. The preferred technology and blinking methods were Video-Oculography (VOG) and long blinks Implementation of this paper successfully bridges the communication gap between the outside world and the paralyzed/disabled patients.

Open CV, eye aspect ratio, Ada boost classifier, face detection, EOG, VOG

[1]. Joshua D. Fischer, Dawie J. van den Heever, ?Portable Video-Oculography Device for Implementation in Sideline Concussion Assessments: A Prototype?, IEEE conference, 2016.
[2]. Sudhir Rao Rupanagudi, Vikas N S, Vivek C Bharadwaj, Manju; Dhruva N; Sowmya K. S, ?Novel methodology for blink recognition using video oculography for communicating?, IEEE Conference Publications, ICAEE, 2014.
[3]. Masaru Kiyama, Hitoshi Iyatomi and Koichi Ogawa, ?Development of robust videooculography system for non-invasive automatic nerve quantification?, IEEE Conference Publications, IEEE-EMBS Conference on Biomedical Engineering and Sciences, 2012.
[4]. S. M. H. Jansen, H. Kingma, R. L. M. Peeters and R. L. Westra, ?A torsional eye movement calculation algorithm for low contrast images in video-oculography ?, IEEE
[5]. Akshat Gambhir, K.S Boppaiah, M Shruthi Subbaiah, Pooja M and KiranP, RNSIT, ?Video Oculographic System using Real-Time Video Processing?, International Journal of Computer Applications (0975 ?8887) Volume 119 ?No.22, June 2015.
[6]. Karthik K.P and Basavaraju S, Department of Electronics & Communication Engineering, Sapthagiri College of Engineering, Bangalore, ?Design and Implementation of Unique Video Oculographic Algorithm Using Real Time Video Processing on FPGA?, IJSRD -International Journal for Scientific Research & Development| Vol. 3, Issue 04, 2015 | ISSN (online): 2321-0613.
[7]. C. D. N. Ayudhya and T. Srinark, ??A method for real-time eye blink detection and its application,?? in Proc. 6th Int. Joint Conf. Comput. Sci.Softw. Eng. (JCSSE), pp. 1?6, 2009.
[8]. P. Biswas and P. Langdon, ??A new interaction technique involving eye gaze tracker and scanning system,?? in Proc. Conf. Eye Tracking South Africa, pp. 67?70, 2013.
[9]. M. Chau and M. Betke, ??Real time eye tracking and blink detection with USB cameras,?? Dept. Comput. Sci., Boston Univ., Boston, MA, USA,Tech. Rep. 2005-12, 2005.
[10]. E. Dalmaijer, ??Is the low-cost EyeTribe eye tracker any good forresearch??? PeerJ PrePrints, vol. 2, , Art. no. e585v1, 2014.
[11]. A. Dementyev and C. Holz, ??Dualblink: A wearable device to continuously detect, track, and actuate blinking for alleviating dry eyesand computer vision syndrome,?? Proc. ACM Interact. Mobile Wearable Ubiquitous Technol., vol. 1, no. 1, pp. 1:1?1:19, Mar. 2017.
[12]. M. Divjak and H. Bischof, ??Eye blink based fatigue detection for prevention of computer vision syndrome,?? in Proc. MVA, pp. 350?353, 2009.
[13]. T. Drutarovsky and A. Fogelton, ??Eye blink detection using varianceof motion vectors,?? in Proc. Eur. Conf. Comput. Vis. Springer, pp. 436?448, 2014.
[14]. K. Wang and Q. Ji, ??Real time eye gaze tracking with Kinect,??in Proc. 23rd Int. Conf. Pattern Recognit. (ICPR), pp. 2752?2757, Dec. 2016.
[15]. R. Valenti and T. Gevers, ??Accurate eye center location and tracking using isophote curvature,?? in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1?8, Jun. 2008.
[16]. X. Zheng, X. Li, J. Liu, W. Chen, and Y. Hao, ??A portable wirelesseye movement-controlled human-computer interface for the disabled,?? in Proc. Int. Conf. Complex Med. Eng. (ICME), Tempe, AZ, USA, pp. 1?5, Apr. 2009.
[17]. T. Soukupov?, ??Real-time eye blink detection using facial landmarks,??in Proc. 21st Comput. Vis. Winter Workshop Luka Cehovin, R. Mandeljc Rimske Toplice, Feb. 20

Aishwarya S., Amulya G.N., Anusha N., Anvitha Kamath, Sushmitha S., "Efficient Eye Blink Detection for Disabled," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.36-40, 2020.

The way of living has modified since the digital age. Everything can be dealt with a tip of the finger, but all these luxuries are at risk at a cost of protection or fraud. Handwritten script or calligraphy explore about a person`s personality. It tells concerning the character of the person and predicts the attribute like optimistic, Social Maturity, balanced, shy within the calligraphy as writing is linked with brain and it subconsciously leaves a sample. Various forms of calligraphy styles taken into thought are slope, baseline, top margin, word size, line spacing, word spacing, left or right or normal slant or irregular of the sentence, etc. The complete system evaluates the script based on the above-mentioned calligraphy styles and it is divided into three modules with the primary module being input the image of written text, then apply the preprocess to removes noise and sharpens the contrast of the image for better results. Extract the 7 features from each image in the dataset, then apply Convolutional Neural Network (CNN) combined with Multi Layer Perceptron(MLP). The proposed system reveals a better result compared to literature survey

Graphology, Personality Traits, Calligraphy, Feature Extraction, CNN, MLP

[1]. Subham Nagar, Sudiksha Chakraborty, Arka Sengupta, ?An efficient method for character analysis using space in handwriting image?,Sixth International Symposium on Embedded Computing and System Design (ISED), pages 210?216. IEEE, 2016.
[2]. Behnam Fallah and Hassan Khotanlou, ?Identify human personality parameters based on handwriting using neural network?,Artificial Intelligence and Robotics (IRANOPEN), pages 120?126. IEEE, 2016.
[3]. Anamika Sen and Harsh Shah, ?Automated handwriting analysis system using principles of graphology and image processing?, International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pages 1?6. IEEE, 2017.
[4]. Bala Mallikarjunarao Garlapati ,Srinivasa Rao Chalamala, ?A system for handwritten and printed text classification?, UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim), pages 50?54. IEEE, 2017.
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[9]. Vaishali R Lokhande and Bharti W Gawali, ?Analysis of signature for the prediction of personality traits?, 1st International Conference on Intelligent Systems and Information Management (ICISIM), pages 44?49. IEEE, 2017.
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Nijil Raj N., Mohammed Thaha, Sushlin Grace Shaji, Shibina S., "Forecasting Personality Based On Calligraphy Using CNN and MLP," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.41-48, 2020.

About 40 percent of the world?s population relies on the agriculture field. Apart from traditional farming, recent years have demanded the application of autonomous vehicles in agriculture. The proposed system aims at designing an autonomous agricultural robot that can be controlled through IoT providing real-time monitoring of the field thereby improving the irrigation system. The system can be controlled by an IoT module. The proposed system aims at reducing the human intervention in farms providing sufficient environmental conditions for the crops with ensuring proper irrigation and efficient utilization of resources. This Agribot can perform the basic functions like monitoring, taking soil parameters, and control the irrigation system accordingly. These robots have a major role in precision farming. The application helped increase investment and using these in real-time. Precision agriculture thus improves agricultural yield and takes precautions in environmental risks demanding the need for agricultural robots. Hence autonomous IoT based agricultural robots are designed to perform the basic elementary functions required to be carried out in fields.

Agribot;Irrigation;IoT;Sensors

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Linta Joseph, Sara Joseph, Sarath H., Shibino P. Abraham, Anish George, "An Autonomous Agriculture Robot Using IoT," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.49-53, 2020.

This era created a change in life style of people and as a result more people are becoming vulnerable to diseases. Completion of medicine course is necessary to evict the disease completely from the body and here our aim is to design a system which can remind and monitor the medication schedule of patients. Medbox is a smart pill box which is an assistive system for patients to schedule their medication timings. It comes along with an android application in which user can customize the each cell in the Medbox. The app provides a provision for alerting the caretaker if the patient forgets to take the pill. The Medbox is designed with 21 cells. Assuming a person is taking medicine 3 times a day. During the medication time, the Medbox alert the user using the red light indicator, buzzer and alarm in the phone. The proper medication details of the patient can be monitored by the caretaker by using the app. There is a separate login portal for patient and caretaker. If the patient goes out without taking the Medbox then, the patients will be notified by an alert message. Medbox and phone is connected via Bluetooth. The Medbox and android application are connected via cloud

Medbox, Patient assistive system, Smart pillbox

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Sibin M.S., Elisha G. Pakalomattom, Haripriya S., Denisious K.D., "Medbox: The Smart Pillbox," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.54-61, 2020.