iosr-JCE   Volume-23 ~ Issue-2 ~ Mar. - Apr. 2021

Abstract: Internet of Things (IoT) connected devices will be reaching people seamlessly in future days. With the passage of time in the context of IoT, many protocols have been devised for secured transmission, including XMPP, CoAP AMQP, ZigBee, 6LoWPAN, LWM2M etc. In Addition to above, The Message Queue Telemetry Transport (MQTT) is extensively used protocols in most IoT based communication network. The security aspects for the IoT domain is an open field of research and analysis. Since, MQTT standard has no mandatory requirements regarding the security services; therefore, manipulating the security issues in MQTT platforms seems very easy. This paper looks to evaluate the proposed security enhanced IoT protocols with the security analysis with a focus to MQTT protocol. Basing on test result and analysis, a Secured Message Queue Telemetry Transport (SMQTT) protocol is proposed..........

Keywords: IoT, MQTT, SMQTT, 6LoPAN, CoAP, ZigBee, Authentication, Encryption, Cryptography, ProVerif

[1]. S. R. J. Ramson, S. Vishnu, and M. Shanmugam, "Applications of Internet of Things (IoT) - An Overview," in 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, Mar. 2020, pp. 92–95, doi: 10.1109/ICDCS48716.2020.243556.
[2]. V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, "A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures," IEEE Access, vol. 7, pp. 82721–82743, 2019, doi: 10.1109/ACCESS.2019.2924045.
[3]. TONIN, M.: The Internet of Things: Promises and Perils of a Disruptive Technology, NATO Report, 2017. https://www.nato-pa.int/document/2017-internet-things-toninreport-175-stctts-17-e-bis (letöltve: 2018. 04. 29.)
[4]. MQTT IoT Protocol complete Tutorial - How it Works with a demo, 1Sheeld | All Arduino shields on your Smartphone, 04-Jul-2018
[5]. M. Singh, M. A. Rajan, V. L. Shivraj and P. Balamuralidhar, "Secure MQTT for Internet of Things (IoT)," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, 2015, pp. 746-751.

Abstract: Internet of things (IoT) is one of the top emerging technologies in the world today, which refers to the billions of physical devices around the world that are now connected to the internet. Internet of Vehicles (IoV) is one of key members of IoT that allows vehicles to exchanges information and use wireless communication to provide more services and applications that serve humans, ensure efficiency, produce safety and security. The payment system is considered as variable element since it is closely linked to the e-commerce and the development of technology. Beside that, the technology has established significant scope to develop the payment system industry according to consumer requirements....

Keywords: Electronic Payment System, Internet of Vehicle, Security Mechanism, Security Requirement

[1]. Jerrin Yomas, Chitra Kiran N. (2018). A Critical Analysis on the Evolution in the E-Payment System, Secuiry Risk, Threats and Vulnerability, Communications on Applied Electronics (CAE), Volume 7– No. 23.
[2]. Zlatko Bezhovski. (2016). The Future of the Mobile Payment as Electronic Payment System, European Journal of Business and Management, Vol.8, No.8, Page 128.
[3]. Craig M. Parker and Paula M. C. Swatman. (2002). Electronic Payment Systems, School of Information Systems.
[4]. Mansi Prakashbhai Bosamia. (2017). Mobile Wallet Payments Recent Potential Threats and Vulnerabilities with its possible security Measures, ResearchGate.
[5]. Kremena Marinova-Kostova (2017). Mobile wallet – functions, components and architecture, ResearchGate, Page 1-2.

Abstract: Background: Diabetes mellitus (also referred to as "diabetes") falls under the category of metabolic disease elevating blood sugar levels, which over time may cause severe damage to the heart, blood vessels, eyes, kidneys, and nerves. A hormone, Insulin that is produced by pancreas tends to move sugar from the blood to cells in the body to store or use for energy. Diabetes is a medical condition that weakens the body's ability to process blood glucose (known as blood sugar). It happens by either inadequate insulin production (Type-1 diabetes) or Inadequate sensitivity of cells to the action of insulin (Type-2 diabetes). Gestational diabetes, a third type, is a kind of diabetes seen in pregnant women and is associated with both mother and child complications. It puts the mother and......

[1]. K. Sumangali, B. S. R. Geetika and H. Ambarkar, "A classifier based approach for early detection of diabetes mellitus," 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2016, pp. 389-392, doi: 10.1109/ICCICCT.2016.7987979.
[2]. Zou Q, Qu K, Luo Y, Yin D, Ju Y and Tang H (2018) Predicting Diabetes Mellitus With Machine Learning Techniques. Front. Genet. 9:515. doi: 10.3389/fgene.2018.00515
[3]. S. Benbelkacem and B. Atmani, "Random Forests for Diabetes Diagnosis," 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1-4, doi: 10.1109/ICCISci.2019.8716405.
[4]. P. S. Kumar and S. Pranavi, "Performance analysis of machine learning algorithms on diabetes dataset using big data analytics," 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, 2017, pp. 508-513, doi: 10.1109/ICTUS.2017.8286062.
[5]. S. Y. Rubaiat, M. M. Rahman and M. K. Hasan, "Important Feature Selection & Accuracy Comparisons of Different Machine Learning Models for Early Diabetes Detection," 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 2018, pp. 1-6, doi: 10.1109/CIET.2018.8660831

Abstract: In this paper we presented a method to nuclei image segmentation and detection on public nuclei cancer images which we used from Kaggle 2018 Data Science Bowl data sets. For segmentation the deep neural network with semantic approaches are used. For accuracy evaluation we used the confusion matrix that calculate the sensitivity, specificity and accuracy. Also, for more details of the analyzing of the performance the JI, Dice, Precision, Recall, and F1 Score are used. We compared the output result with the ground truth images. For each pixel we calculated these criteria. The average result for sensitivity, specificity and accuracy was 96.67, 95.97 and 97.78 respectively.

Keywords: Image segmentation, Nuclei Cells Detection, Deep Neural Network.

[1] N. A. Irwin, "The functional diversity and evolution of nuclear processes," University of British Columbia, 2020.
[2] M. Saha and C. Chakraborty, "Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation," IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2189-2200, 2018.
[3] J. Gamper, N. A. Koohbanani, K. Benet, A. Khuram, and N. Rajpoot, "PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification," in European Congress on Digital Pathology, 2019: Springer, pp. 11-19.
[4] Q. D. Vu et al., "Methods for segmentation and classification of digital microscopy tissue images," Frontiers in bioengineering and biotechnology, vol. 7, p. 53, 2019.
[5] F. A. Alsarori, H. Kaya, J. Rahebi, D. E. Popescu, and D. J. Hemanth, "Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms," International Journal of Computational Intelligence Systems, 2020.

Abstract: Postnatal Depression is a serious problem found silently in our society which affects a large amount of women. The awareness and diagnosis of postnatal depression is very less concern for the patients after delivery. Data mining techniques can help to diagnosis the probable conditions of patients suffering from Postnatal Blues, postnatal Depression or postnatal psychosis. Data mining is known for its techniques and predictive solution for early detection of postpartum depression. Mostly widely techniques are Random Forest, C4.5, Support Vector Machine and many more.

[1]. Vanishee Shriraam, Pankaj Badamilai Shah, Anitha Rani M.,B.W.C. Sathiyasekaran, A Community based Study of Postpartum Depression in Rural Southern India, July 26
[2]. Aris Supriyanto,Suryono Suryono, Jatmiko Endro Susesno,Implementation Data Mining using Decision Tree Method-Algorithm C4.5 for Postpartum Depression Diagnosis, 2018, E3S Web of Conferences 73,12012
[3]. Dayeon Shin,Kyung ju Lee,Temidayo Adeluwa and Junguk Hur, Machine Learning Based Predictive Modeling of postpartum depression, Journal of Clinical Medicine, 22 August
[4]. Mei Cai, Yiming Wang, Qian Luo and Guo Wei, Factor Analysis of the Prediction of the Postpartum Depression Screening Scale, International Journal of Environmental Research and Public Health,2019
[5]. Mário W.L. Moreira , Joel J.P.C. Rodrigues , Neeraj Kumar , Kashif Saleem , Igor V. Illin, Postpartum Depression Prediction through Pregnancy Data Analysis for Emotion-aware Smart Systems

Abstract: The main motivation to provide location-based services(LBS) that include identifying the user locations and Storing large amounts of data with cloud service providers (CSPs) concern about data protection .The purpose of the work is provide a implementation methodology for location based services ,examine the data and restoration which is stored in cloud. To solve the particular areas problems like water problem, electricity problem, transport problem and sewage problems in a particular area. User files the complaint along with location in the website that will be moved to the particular department. Then admin will view the user complaints and then based on station the respective admin will respond to user regarding the complaints. Once the problem solved that information....

[1]. G. Sun et al., "Location Privacy Preservation for Mobile Users in Location-Based Services", IEEE Access, Vol. 7, 2019 [2]. W. I. Khedr, H. M. Khater and E. R. Mohamed, "Cryptographic Accumulator-Based Scheme for Critical Data Integrity Verification in Cloud Storage", IEEE Access, Vol. 7, 2019.
[3]. R. Moreno-Vozmediano, R.S. Montero and I.M. Llorente, "Key Challenges in Cloud Computing to Enable the Future Internet of Services", IEEE Internet Computing, vol. 17, no .4, July/Aug.2013.
[4]. B. Wang, B. Li, and H. Li, "Certificate less Public Auditing for Data Integrity in the Cloud", Proc. IEEE Conf. Comm. and Network Security (CNS'13), pp. 276-284, 2013.
[5]. C. Wang, S.S. Chow, Q. Wang, K. Ren , and W. Lou, "Privacy-Preserving Public Auditing for Secure Cloud Storage", IEEE Trans.
Computers, vol. 62, no. 2, pp. 362-375, Feb. 2013.

Abstract: While big data analytics is being increasingly used to gain data-driven insights to support decision-making, little research exists regarding the impact of Big Data Analytics on decision-making and effectiveness of decisions on organizational performance. Drawing on the information processing view and survey based ratings; this paper develops a research model linking Big Data Analytics to organizational decision-making effectiveness and performance of organizations in manufacturing sector of Uzbekistan. Study uses the Simon's decision making theory to lens the intellectuals of big data analytics and decision making. Paper aims to correlate BDA with decision making of an organization and aims to observe impact of BDA on decision making. For the purpose manufacturing industry of Uzbekistan has been selected. Data collection will be based on self made questionnaire. Statistical treatment of the data finds that Big Data Analytics.........

Key words: Big Data Analytics, Decision Making & Trade Data

[1]. Adnan, A. (2017). Factors affecting the organizational performance of manufacturing firms. International Journal of Engineering Business Management , pp. 12-16.
[2]. Ara, I. (2019). Is Uzbekistan 's Manufacturing Sector Competitive? Uzbekistan Development Review , pp. 23-34.
[3]. Augier, M. (2000). Models of Herbert A. Simon. Perspectives in Science , pp. 407-443.
[4]. Bean, R. (2013). Organizational Alignment is Key to Big Data Success. Boston: MIT Sloan Management Reviews .
[5]. Bifet, W. F. (2012). Mining Big Data: Current Status, and Forecast to the Future. SIGKDD Explorations , pp.-2-3.