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Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk.. Machine learning and data extracted from one 20-second phase of the task are used to predict diagnosis in a large sample of children with and without an internalizing diagnosis. Nevertheless, the proposed approach provides a rapid, objective, and accurate means for diagnosing internalizing disorders in young children. This new approach reduces the time required for diagnosis while also limiting the need for highly trained personnel – each of which can help to reduce the length of waitlists for child mental health services. While these results can likely be improved and extended, this is an important first step in reducing the barriers associated with assessing young children for internalizing disorders.

ML, KNN, LAN

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[3] M. Tandon, E. Cardeli, and J. Luby, “Internalizing Disorders in Early Childhood: A Review of Depressive and Anxiety Disorders,” Childand Adolescent Psychiatric Clinics of North America, vol. 18, no. 3, pp. 593–610, Jul. 2009.
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[5] D. J. Whalen, C. M. Sylvester, and J. L. Luby, “Depression and
Anxiety in Preschoolers: A Review of the Past 7 Years,” Child and Adolescent Psychiatric Clinics of North America, vol. 26, no. 3, pp.503–522, Jul. 2017.
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A systematic review,” Journal of Affective Disorders, vol. 114, no. 1,
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in anxiety-disordered children,” J Anxiety Disord, vol. 11, no. 4, pp. 347–363, Aug. 1997.
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[17] D. Cawthorpe et al., “Priority-Setting for Children’s Mental Health: Clinical Usefulness and Validity of the Priority Criteria Score,” J CanAcad Child Adolesc Psychiatry, vol. 16, no. 1, pp. 18–26, Feb. 2007.
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Rushmitha K, Sravani V, Jyothsana. R, Harsha. A.C, "Diagnose Anxiety and Depression in young children using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.165-170, 2019.

Automation has a wide role in the current generation which can be deployed into cars making them drive on their own by considering the surrounding environment as input parameters. Detection of lanes using canny edge lane detection algorithm helps to detect lanes and ensure the drivable space and have clear information of lane in which the car is moving. Deep Neural Networks helps in deciding the action to be performed by the car (forward, reverse, right, left, stop, and park). This paper covers motion control, path detection and obstacle detection. The results have been achieved by the implementation of Canny Edge Detection Algorithm, Deep Neural Networks Techniques.

Deep Neural Network, Canny Edge Detection Algorithm, Obstacle Detection

[1] TU-Automotive, “Driverless vehicles will continue to dominate auto headlines tu automotive [online],” April, 2016, available: http://analysis.tu-auto.com/autonomous-car/driverless-vehicles-willcontinue- dominate-auto-headlines. [Accessed: 10-April-2018].
[2] L. Fridman, D. E. Brown, M. Glazer, W. Angell, S. Dodd, B. Jenik, J. Terwilliger, J. Kindelsberger, L. Ding, S. Seaman, H. Abraham, A. Mehler, A. Sipperley, A. Pettinato, B. Seppelt, L. Angell, B. Mehler, and B. Reimer, “Mit autonomous vehicle technology study: Large-scale deep learning based analysis of driver behavior and interaction with automation,” Nov 2017, available:https://arxiv. org/abs/1711.06976.
[3] WHO, “Global status report on road safety 2015. world health organization,” 2015.
[4] Saha, Anik, et al. "Automated road lane detection for intelligent vehicles." Global Journal of Computer Science and Technology (2012).
[5] Pannu, Gurjashan Singh, Mohammad Dawud Ansari, and Pritha Gupta. "Design and implementation of autonomous car using Raspberry Pi." International Journal of Computer Applications 113.9 (2015)
[6] Mohanapriya, R., Hema, L. K., Yadav, D., & Verma, V. K. (2014). Driverless Intelligent Vehicle for Future Public Transport Based On GPS. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3.
[7] Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino Aditya Kumar Jain Electronics and Communication Department Dharmsinh Desai University Gujarat, India
[8] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,D. Anguelov, D.Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pages 1–9, 2015.
[9] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. arXiv:1712.05877, 2017.

Sharmila S, Shivaswaroop S, Sudhakar M, Tejashwini S V, Rajshekhar S A, "Self Driving Car Using Deep Neural Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.171-176, 2019.

Every living being requires energy for which it depends on food. Human population rely on agriculture, one of the main sources of food. In this project the main focus is to aid agricultural activities using IOT techniques in an effective and user-friendly manner. The system here consists of an agricultural robot which performs grass cutting, ploughing, seeding along with obstacle detection. All these functions of Agribot is chosen using user-friendly mobile application. Agribot uses Renesas microcontroller, DC motors for wheel rotation, to rotate cutting blades, to open or close seeding valve and to move the ploughing arm. Ultrasonic sensor is used for obstacle detection and android app is the mobile application. Bluetooth module is used to communicate between the app and Agribot. The function selected by the user is also displayed on the LCD of the Agribot. Agribot thereby helps to increase profit margins of farmers with minimal investments.

Internet of Things (IoT), Agribot, Ploughing, Seeding, Grass cutting, Bluetooth, Ultrasonic sensor and Mobile application

[1] Md. Didarul Islam Sujon, Rumman Nasir, Mahbube Mozammel Ibne Habib, Majedul Islam Nomaan, Jayasree Baidya, Md. Rezaul Islam “Agribot: Arduino Controlled Autonomous Multi-Purpose Farm Machinery Robot for Small to Medium Scale Cultivation”,2018 IEEE, ISBN: 978-1-5386-6332-5 DOI: 10.1109/ICoIAS.2018.8494164
[2] Dasari Naga Vinod, Tripty Singh“Autonomous Farming and Surveillance Agribot in Adjacent Boundary”,2018, IEEE
ISBN: 978-1-5386-4431-7 DOI: 10.1109/ICCCNT.2018.8494137
[3] Rahul D S, Sudarshan S K, Meghana K, Nandan K N, R Kirthana , Pallaviram Sure,“IoT based Solar Powered Agribot for Irrigation and Farm Monitoring Agribot for Irrigation and Farm Monitoring” , 2018, IEEE, ISBN: 978-1-5386-0808-1 DOI: 10.1109/ICISC.2018.8398915
[4] Konlakorn Wongpatikaseree, Promprasit Kanka, Arunee Ratikan
“Developing Smart Farm and Traceability System for Agricultural Products using IoT Technology”, 2018, IEEE
ISBN: 978-1-5386-5893-2 DOI: 10.1109/ICIS.2018.8466479
[5] K Durga Sowjanya, R Sindhu, M Parijatham, K Srikanth, P Bhargav
“Multipurpose autonomous agricultural robot”, 2017, IEEE
ISBN: 978-1-5090-5687-3 DOI: 10.1109/ICECA.2017.8212756
[6] K.Lokesh Krishna , Omayo Silver, Wasswa Fahad Malende
“Internet of Things application for implementation of smart agriculture system”, 2017, IEEE , ISBN: 978-1-5090-3244-0 DOI: 10.1109/I-SMAC.2017.8058236

Vasudha Hegde, Sumathi M, Varsha Nagarajaiah, Yeshaswini M.C, Chandan Raj B R, "IOT Based Agribot for Agricultural Farming", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.177-182, 2019.

Client action logs can be a profitable wellspring of data in cloud legal examinations .thus, guaranteeing the unwavering quality and security of such logs is significant. Most existing proposition for secure logging is intended for conventional environment as opposed to the perplexing condition of the cloud. The log documents present in the log records ought to be protected from aggressors and outsider associations. Log Files contains private data about the association`s action. So as to conquer the assaults from outside elements, In this paper, we propose the Efficient and secure log secrecy schemefor cloud forensics process as an elective plan for the verifying of logs in a cloud domain. In Efficient and secure log secrecy scheme for cloud forensics, logs are encrypted utilizing the individual client`s public key with the goal that just the client can decrypt the substance. So as to avert unapproved change of the log, we produce proof of past log (PPL) using cryptography techniques.

Logs,Cloud,cryptography

[1] X. Liu, R. H. Deng, K.-K. R. Choo, and J. Weng, "An efficient privacy-preserving outsourced calculation toolkit with multiple keys," IEEE Transactions on Information Forensics and Security, vol. 11, pp. 2401-2414,2016.
[2] Y. Mansouri, A. N. Toosi, and R. Buyya, "Data storage management in cloud environments: Taxonomy, survey, and future directions," ACM Computing Surveys(CSUR), vol. 50, p. 91, 2017.
[3] M. Tao, J. Zuo, Z. Liu, A. Castiglione, and F. Palmieri, "Multi-layer cloud architectural model and ontology-based security service framework for IoT-based smart homes,"Future Generation Computer Systems, vol. 78, pp. 1040-1051,2018.
[4] Z. Xia, Y. Zhu, X. Sun, Z. Qin, and K. Ren, "Towards privacy-preserving content-based image retrieval in cloud computing," IEEE TransactionsonCloudComputing,pp.276-286, 2018.
[5] L. Zhou, Y. Zhu, and A. Castiglione, "Efficient k-NN query over encrypted data in cloud with limited key-disclosure and offline data owner," Computers&Security,vol.69,pp.84-96,2017.
[6] Q. Alam, S. U. Malik, A. Akhunzada, K.-K. R. Choo, S. Tabbasum, and M. Alam, "A Cross Tenant Access Control (CTAC) Model for Cloud Computing: Formal Specification and Verification,"IEEETransactionsonInformation Forensics and Security, vol. 12, pp. 1259-1268, 2017.
[7] L.Li,R.Lu,K.-K.R.Choo,A.Datta,andJ.Shao, "Privacy-preserving-outsourced association rule mining on vertically partitioned databases," IEEE Transactions on Information Forensics and Security, vol. 11, pp. 1847-1861, 2016.
[8] F.Anwar and Z.Anwar "Digital forensics for eucalyptus," in Frontiers of Information Technology (FIT), 2011, 2011, pp.110-116.
[9] A. Patrascu and V.-V. Patriciu, "Loggingsystem for cloud computing forensic environments," Journal of Control Engineering and Applied Informatics, vol. 16, pp. 80-88,2014.
[10] M. Bellare and B. Yee, "Forward integrity for secure audit logs," Technical report, Computer Science and Engineering Department, University of California at SanDiego1997.
[11] B. Schneier and J. Kelsey, "Secure audit logs to support computer forensics," ACM Transactions on Information and System Security (TISSEC), vol. 2, pp. 159-176,1999.
[12] S. Zawoad, A. K. Dutta, and R. Hasan, "Towards building forensics enabled cloud through secure logging-as-a-service," IEEE Transactions on Dependable and Secure Computing, vol. 13, pp. 148-162,2016.

Vijeth M, Vikas H R, Vishwas B S,Vishwas N, Prasanna Kumar M, "Efficient and Secure Cloud Log Secrecy Scheme for Cloud Forensics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.183-186, 2019.

As urbanization is spreading quickly, there is an expansion in the creation of waste. Squander the board is a vital issue to be considered at open spots where squander is flooded from the containers and may cause distinctive ailments. The present work centers to build up a model of shrewd dustbin which can be viably utilized at open places in smart cities. The model has a self-compressing system which compresses the trash when the trash can gets filled up thus it creates more room for further disposal. At whatever point any dustbin is topped off, a message is sent to the concerned authority and to the trash collecting truck that garbage bin is completely crowded and needs obligatory attention. This might facilitate to manage the rubbish assortment with efficiency. This will avoid the flood of waste in the container and it avoids the disposal of the wastes around the disposal site which are the main aims of this paper. In our System, the refuse level in the dustbins will be detected with the assistance of the Ultrasonic sensor and it has GPS module which provides the exact location of the smart bin and communication to the authorized control room is done through a GSM using Arduino Mega 2560 and the Blynk framework

Self-compressing system, Ultrasonic sensor,GPS,GSM, Arduino Mega 2560, Blynk

[1] Dr.N.S.kumar, “IOT Based Smart Garbage alert system using g Arduino UNO”, IEEE Region 10 Conference (TENCON) — Proceedings of the International Conference,p-1028-1034, 2016.
[3] Theodoros Anagnosto poulosa, “Assessing dynamic models for high priority waste collection in smart cities”, The Journal of Systems and Software 110 ,p: 178–192 , 2015.
[4] F. Folianto, “Smartbin:Smart Waste Management System”, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Demo and Video Singapore, 7-9 April 2015.
[5] M. Aazam, "Cloud-based Smart Waste Management for Smart Cities ",978-1-5090- 2558-9/16/$31.00 ,IEEE, 2016.
[6] Nesreen Alsbou, Mohamed Abdul Samad, Mohammed Alhashem, Alaeddin S.A. Abuabed “Developing a Self- Powered Enlarging Smart Waste Bin”14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018 Pages:683 – 689.



[7] G Sai Rohit, M Bharat Chandra, Shaurabh Saha, Debanjan Das, “Smart Dual Dustbin Model for Waste Management in Smart Cities”,3rd International Conference for Convergence in Technology (I2CT), 2018 Pages: 1 – 5.
[8] A. Sathish, M. Prakash, S. A. K Jainulabudeen ,
R. Sathishkumar, “Intellectual trash management using Internet of Things”,International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC),2017 Pages: 053 – 057.
[9] N. S. Kumar, B. Vijayalakshmi, R. J. Prarthana and A. Shankar, “IOT Based Smart Garbage Alert System Using Arduino UNO,” in IEEE Region 10 ConferenceProceedings of the International Conference, TENCON 2016, Singapore, November 22-25, 2016, IEEE Xplore, 2017. pp. 1028-1034.
[10] T. Sinha, K. M. Kumar and P. Saisharan, “Smart Dustbin,” international Journal of Industrial Electronics and Electrical Engineering, vol. 3, pp. 101-104, May 2015.
[11] M. A. Maehlum, “Which Solar Panel Type is Best? Mono- vs. Polycrystalline vs. Thin Film,” Energy Informative, August 5, 2017.
[12] J. Joshi, J. Reddy, P. Reddy, A. Agarwal, R. Agarwal,
A. Bagga and A. Bhargava, “Cloud Computing Based Smart Garbage Monitoring System,” in 3rd international Conf. Electronic Design, ICED 2016, Phuket, Thailand, August 11- 12, 2016, IEEE Xplore, 2017 pp. 70-75.
[13] W. Reshmi, R. Sundaram and M. R. Kumar, “Sensor Unit for Waste Management: A Better Method for Frequent Data Updating System,” in international Conference on Science, Engr. and Management Research, ICSEMR 2014, Chennai, India, November 27-29, 2014, IEEE Xplore, 2015. pp. 1-5.
[14] N. Gupta, K. Mangla, A. K. Jha and Md. Umar, “Applying Dijikstra’s Algorithm in Routing Process,” International Journal of New Technology and Research (IJNTR), vol. 2, pp. 122-124, May 2016.
[15] G. Qing, Z. Zheng and X. Yue, “Path-Planning of Automated Guided Vehicle Based on Improved Dijkstra Algorithm,” in 29th Chinese Control and Decision Conference, CCDC 2017, Chongqing, China, May 28-30, 2017, IEEE Xplore, 2017. pp. 7138-7.
[16] “Connecting Waste,” Technology and Operations Management, November 18, 2016.

Sachin K N, Tejaswini D S, Suraj N R, Rajshekar S A, "Self-Compressing Smart Trash Bin", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.187-191, 2019.

Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEHIoT) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this proposed project, we present an overview of enabling technologies for efficient WEHIoT, analyze the life-time of WEH-enabled IoT devices.

IOT, Node MCU, Relays, GasSensor

[1] Intel. (2017) The internet of things starts with intel inside. [Online]. Available: https://www.intel.com/content/ www/us/en/internet-of-things/overview.html?cv=1&session-id= a72c71a6dead059d17510ab183b548c4
[2] Intel. Guide to iot, year = 2017, url = http://www.intel.com/content/www/us/en/internet-ofthings/infographics/guide-to-iot.html, urldate = 2017-11-14.
[3] K. Wang, Y. Wang, Y. Sun, S. Guo, and J. Wu, “Green industrial internet of things architecture: An energy-efficient perspective,” IEEE Communications Magazine, vol. 54, no. 12, pp. 48–54, Dec. 2016.
[4] H. Jayakumar, K. Lee, W. S. Lee, A. Raha, Y. Kim, and V. Raghunathan, “Powering the internet of things,” in Proceedings of the 2014 international symposium on Low power electronics and design. ACM, 2014, pp. 375–380.
http://www.beechamresearch.com/article.aspx?id=4
[6] F. Akhtar and M. H. Rehmani, “Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review,” Renewable and Sustainable Energy Reviews, vol. 45, pp. 769 – 784, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032115001094
[7]“Energy harvesting for self-sustainable wireless body area networks,” IT Professional, vol. 19, no. 2, pp. 32–40, March 2017.
[8] S. Chalasani and J. M. Conrad, “A survey of energy harvesting sources for embedded systems,” in IEEE SoutheastCon 2008. IEEE, 2008, pp. 442–447.
[9] F. Yildiz, “Potential ambient energy-harvesting sources and techniques,” 2009.
[10] V. Raghunathan and P. H. Chou, “Design and power management of energy harvesting embedded systems,” in Proceedings of the 2006 international symposium on Low power electronics and design. ACM, 2006, pp. 369–374.
[11] D. Pimentel and P. Mus´ılek, “Power management with energy harvesting devices,” in Electrical and Computer Engineering (CCECE), 2010 23rd Canadian Conference on. IEEE, 2010, pp. 1–4.
[12] W. K. Seah, Z. A. Eu, and H.-P. Tan, “Wireless sensor networks powered by ambient energy harvesting (wsn-heap)-survey and challenges,” in Wireless Communication, Vehicular Technology, Information Theory and Aerospace &Electronic Systems Technology, 2009. Wireless VITAE 2009. 1st International Conference on. Ieee, 2009, pp. 1–5.
[14] S. Sudevalayam and P. Kulkarni, “Energy harvesting sensor nodes: Survey and implications,” IEEE Communications Surveys & Tutorials, vol. 13, no. 3, pp. 443–461, 2011.
[15] M. Shirvanimoghaddam, M. Dohler, and S. J. Johnson, “Massive nonorthogonal multiple access for cellular iot: Potentials and limitations,” IEEE Communications Magazine, vol. 55, no. 9, pp. 55–61, 2017.
[16] E. Le Sueur and G. Heiser, “Dynamic voltage and frequency scaling: The laws of diminishing returns,” 2010.
[17] M. D. Yin, J. Cho, and D. Park, “Pulse-based fast battery iot charger using dynamic frequency and duty control techniques based on multisensing of polarization curve,” Energies, vol. 9, no. 3, p. 209, 2016.
[18] S. F. Abedin, M. G. R. Alam, R. Haw, and C. S. Hong, “A system model for energy efficient green-iot network,” in International Conference on Information Networking (ICOIN). IEEE, 2015, pp. 177–182.
[19]Y. Zeng, B. Clerckx, and R. Zhang, “Communications and signals design
for wireless power transmission,” IEEE Trans. Commun., vol. 65, no. 5,pp. 2264–2290, May 2017.
[20] I. Krikidis, S. Timotheou, S. Nikolaou, G. Zheng, D. W. K. Ng, andR. Schober, “Simultaneous wireless information and power transfer inmodern communication systems,” IEEE Commun. Mag., vol. 52, no. 11,pp. 104–110, Nov. 2014.
[21] K. Huang, C. Zhong, and G. Zhu, “Some new research trends inwirelessly powered communications,” IEEE Wireless Commun., vol. 23,no. 2, pp. 19–27, Apr. 2016.
[22] F. Yuan, S. Jin, K.-K. Wong, J. Zhao, and H. Zhu, “Wireless informationand power transfer design for energy cooperation distributed antennasystems,” IEEE Access, vol. 5, pp. 8094–8105, 2017.
[23] D. W. K. Ng and R. Schober, “Secure and green SWIPT in distributedantenna networks with limited backhaul capacity,” IEEE Trans. WirelessCommun., vol. 14, no. 9, pp. 5082–5097, Sep. 2015.
[24] S. Lee, L. Liu, and R. Zhang, “Collaborative wireless energy andinformation transfer in interference channel,” IEEE Trans. WirelessCommun., vol. 14, no. 1, pp. 545–557, Jan. 2015.
[25] F. Yuan, S. Jin, Y. Huang, K. Wong, Q. T. Zhang, and H. Zhu, “Jointwireless information and energy transfer in massive distributed antennasystems,” IEEE Commun. Mag., vol. 53, no. 6, pp. 109–116, Jun. 2015.

Supriya M, Chetana Srinivas, "A Design and Implementation of Wireless Energy Harvesting (WEHIoT)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.192-197, 2019.

PHR is a patient-centric approach of health information exchange, that allows to store, access and to share the personal health information. To share confidential resources at the optimal cost, the PHR service providers are willing to keep the health information in the cloud. Some of the private agencies can expose the health information to some unauthorized persons because patient will not be having the physical control of the PHR. So To Overcome this problem, CipherText-Policy Attribute Based Signcryption is employed for sharing the PHR. It provides a access Control, confidentiality, authenticity of the Information. But it brings a high computational overhead and low efficiency in designcryption process. so some of the major computation are given to the Ciphertext Transformed Server that leaves only a small burden to the PHR User.The system is also capable of computing some unexpected Computations. Futhermore theoretical analysis and desired security properties includes confidentiality, unforgetability and verifiability has been proved in random oracle model.

Personal health record system, Attribute-based signcryption, Cloud computing, Outsourcing computation

[1] A. Sahai, B. Waters et al., “Fuzzy identity-based encryption.” In Eurocrypt, vol. 3494. Springer, 2005, pp. 457–473.
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[3] A. B. Lewko and B. Waters, “Unbounded hibe and attribute-based encryption.” in Eurocrypt, vol. 6632. Springer, 2011, pp. 547–567.
[4] J. Bethencourt, A. Sahai, and B. Waters, “Ciphertext-policy attribute-based encryption,” in Security and Privacy, 2007.SP’07.IEEE Symposium on. IEEE, 2007, pp. 321–334.
[5] A. B. Lewko, T. Okamoto, A. Sahai, K. Takashima, and B. Waters, “Fully secure functional encryption: Attribute-based encryption and (hierarchical) inner product encryption.” in Eurocrypt, vol. 6110. Springer, 2010, pp. 62–91.
[6] H. K. Maji, M. Prabhakaran, and M. Rosulek, “Attribute-based signatures: Achieving attribute-privacy and collusion-resistance.”IACR Cryptology ePrint Archive, vol. 2008, p. 328, 2008.
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Chethan V, Vijay Kumar N, Karan Deep SV, Pallavi, Jagadeesh BN, "Efficient Signcryption with Verifiable DesigncryptionFor Sharing Personal Health Record", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.198-202, 2019.

Medical imaging provides proper diagnosis of brain tumour. Various techniques are implemented to detect the brain tumour from MRI images. One among them is the Denoising wavelet transform (DWT) method which is used to improve the accuracy of a prediction model by making use of MRI images in order to predict the overall survival time of brain tumour patients. Wavelet transform method detects the location and size of the tumour. The proposed methodology consists of image acquisition, Calculation of tissue density maps, statistical analysis. MRI provides generous information about the human soft tissue, which helps in the recognition of brain tumour. Image Segmentation categorises pixels into sections and hence defines the object regions. This paper proposes the image and feature fusion techniques to improve the accuracy of the prediction model.

Machine learning, Denoising wavelet transform, MRI images, Histogram, Glioma brain tumour, Linear Regression

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Saniya Suhail, Savita S Dodakenchannavar, Shilpashree GL, Swetha M, Hemanth YK, "Improving Prediction Model Accuracy by Wavelet Transform Based on MRI Images of Brain Tumour Patients", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.203-207, 2019.

Given the ubiquity of social media, interest-based interactivity as a main element to intensify user experience. Interest-based relevance modeling is taken out from user influence in multiple-platform social network Big Data container. The main goal of this work is to implement a platform for providing recommendation across different social network based on user interest. The streams consisted of tags from social media content through a discovery process and the application is tested on social media content streams to generate a Big Data scenario.

Cross platform, Data security, Big Data

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Iqbal Ansari, Sunil Ghimire, Subash Chaudhary, Anoop N Prasad, "Interest Based Interactivity Through Cross Platform in Big Data", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.208-212, 2019.

The idea of designing and implementation of security against ATM theft is born with the observation of our real life incidents happening around us. This project deals with prevention of ATM crimes and hence overcome the drawback found in existing technology in our society. This paper uses machine learning to enhance the security in ATM. When any suspicious activities such as a man holding a gun in his hand is detected using ORB algorithm, a person attempting to close the camera at ATM, more than two persons entering into ATM, fighting scenes happening at ATM will be detected as an unusual activity and alarm is raised at ATM and a message is passed to nearest police station. Parallelly an email consisting a snap of unusual activity will be sent to the registered police official e-mail id, this helps the police officers to analyze the situation and overcome the fake alarms.

Unusual Activity, Machine Learning, ATM security

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Tejas D, Varshini K, Sushmitha U, Sunandha V.K, "Detection of Unusual Activites at ATM Using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.213-216, 2019.