Volume-10 , Issue-5 , May - 2022 Go Back

This project is aimed at developing a web-based portal named as automated inventory management with stock Optimization system for handling the stock system of any retail organisation. The stock management with stock Optimization device refers back to the machine and tactics to control the inventory of enterprise. This system can be used to keep the info of the inventory, stock renovation, update the inventory primarily based on the income information, generate income and inventory file each day or weekly based, generating invoice and also sending notification or mail earlier than expiry date of a product for giving it priority. In this system we are solving different problem affecting to sales management and purchase management. Automated Inventory Management with Stock Optimization System is important to ensure quality control in businesses that handle transactions resolving around consumer goods. Without proper inventory control, a large retail store may runout of stock on an important item. A good inventory management with stock optimization system will alert the wholesaler when it is time to record. Inventory Management with Stock Optimization System is also on important means of automatically tracking large shipment. An automated Inventory Management with Stock Optimization System helps to minimize the errors while recording the stock.

Stock Optimization, Inventory Management, Application, Web-based, Expiry date

[1] P. Khobragade, R. Selokar, R. Maraskolhe and Prof.M. Talmale “Research paper on Inventory management system”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056, p-ISSN: 2395-0072, Volume: 05, Issue: 04, pp.252-254, Apr-2018.
[2] Rafat Ara, Md. Abdur Rahim, “An online based inventory management system implementation in printing business”, Journal of Emerging Technologies and Innovative Research, Vol. 5, Issue 11, pp. 176-179, 2018.
[3] Varalakshmi G S, Asst Prof. Shivaleela S, “A Review of Inventory Management System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 10, Issue 6, pp. 421-423, June 2021.
[4] M. Mascarenhas, A. Lamani, C. Matkar, A.R. Dessai and A. Kotharkar, “An Automated Inventory Management System”, International Journal of Computer Applications 176(14), pp:21-23, April 2020.
[5] I. Jayanth, V. Sampathkumar, “A descriptive study on inventory control management inconstruction industries”, International Research Journal of Engineering and Technology (IRJET), Vol: 05 Issue: 02 | Feb-2018.
[6] Prof. V. Shinde, A. Singh, K. Wayal, V. Vadhavinde, “Web App Store for Inventory Management and Stock Report in Android App Using Centralized Database”, International Journal for Research in Engineering Application & Management (IJREAM) ISSN : 2454-9150 Special Issue - iCreate April – 2018, pp. 69-73, 2018.
[7] E S Soegoto and A F Palalungan, “Web Based Online Inventory Information System”, 2020 IOP Conf. Ser.: Mater. Sci. Eng. 879 012125, pp.1-6, 2020.
[8] Khalid, F. A., & Lim, S. R. (2018), “A Study on Inventory Management towards Organizational Performance of Manufacturing Company in Melaka”, International Journal of Academic Research in Business and Social Sciences, Vol. 8, Issue. 10, pp. 1216–1227, 2018.
[9] A. Suraj Kumar, Y. Naga Surya Kiran, P. Sirisha, CH. Srinivas, Sheik Gousia Begum, "Implementation of College Management System", International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.74-77, 2020.
[10] Amanze Bethran Chibuike, Nwoke, Bethel Chinenye, Eleberi Leticia E., "An Online Departmental Fee Management System", International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.103-109, 2020.

Sumit Kumar Bera, Souvik Pal, Vishal Kashyap, Vishal Kumar, Yachna Raj, Sudipta Sahana, "Automated Inventory Management with Stock Optimization System," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.1-6, 2022.

As cloud computing becomes more popular as an attractive alternative to traditional information processing systems, accurate and continuous operation is becoming more important, even in the presence of faulty components. This white paper presents an innovative modular system-level perspective for building and managing cloud fault tolerance. It uses a dedicated service layer to provide application developers and users with a comprehensive, high-level approach to hiding the details of implementing fault-tolerance techniques. In particular, the service layer allows users to specify and apply the required level of fault tolerance without the need for knowledge of the fault-tolerance technologies available in the desired cloud and their implementation. Use run-time monitoring to determine the mechanism and characteristics of your fault-tolerance solution. Based on the proposed approach, design a framework that easily integrates with your existing cloud infrastructure and makes it easier for third parties to provide fault tolerance as a service. Our goal is to work directly at the VM instance level of Virtual Machine Manager and inject the framework as a dedicated service layer between the application and the hardware. Fault Tolerance Manager addresses the issue of computing resource inhomogeneity, achieves the goal of transparently providing fault tolerance support for node failures to user applications, and achieves scalability and interoperability goals. To overcome these challenges, we propose to build a fault tolerance manager according to the principles of service-oriented architecture.

Cloud computing, Fault tolerance , Virtual machine, Service-oriented architecture

[1] K. V. Vishwanath and N. Nagappan, “Characterizing cloud computing hardware reliability,” in Proc. 1st ACM Symp. Cloud Comput., pp.193-204, 2010.
[2] R. Jhawar, V. Piuri, and M. D. Santambrogio, “A comprehensive conceptual system-level approach to fault tolerance in cloud computing,” in Proc. IEEE Int. Syst. Conf., Mar. 2012.
[3] W. Zhao, P. M. Melliar-Smith, and L. E. Moser, “Fault tolerance middleware for cloud computing,” in Proc. 3rd Int. Conf. Cloud Comput., pp.67-74, Jul. 2010.
[4] Y. Mao, C. Liu, J. E. van der Merwe, and M. Fernandez, “Cloud resource orchestration: A data-centric approach,” in Proc. 5th Biennial Conf. Innovative Data Syst. Res., 2011.
[5] G. Koslovski, W.-L. Yeow, C. Westphal, T. T. Huu, J. Montagnat, and P. Vicat-Blanc, “Reliability support in virtual infrastructures,” in Proc.IEEE 2nd Int. Conf. Cloud Comput. Technol. Sci., Nov. 2010.
[6] S. De Capitani di Vimercati, S. Foresti, S. Jajodia, S. Paraboschi, G.Pelosi, and P. Samarati, “Encryption-based policy enforcement for cloud storage,” in Proc. 30th Int. Conf. Distributed Comput. Syst. Workshop, 2010.
[7] P. Samarati and S. De Capitani di Vimercati, “Data protection in outsourcing scenarios: Issues and directions,” in Proc. 5th ACM Symp.Inform. Comput. Commun. Security, 2010.
[8] C. A. Ardagna, E. Damiani, R. Jhawar, and V. Piuri, “A model-based approach to reliability certification of services,” in Proc. 6th IEEE Int. Conf. Digit. Ecosyst. Technol., Jun. 2012.
[9] Yookesh, T. L., et al. "Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation." Materials today: Proceedings 47: 6101-6104, 2021.
[10] Kumar, E. Boopathi, and V. Thiagarasu. "Segmentation using Fuzzy Membership Functions: An Approach." IJCSE, Vol.5. Issue.3, pp.101-105, 2017.

Sathishkumar D., R. Vadivel, "Fault Tolerance Middleware Cloud Computing In Virtual Infrastructures," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.7-12, 2022.

Newborn children who brought into the world formerly 36 weeks from growth time mount are identified as untimely infants. Pre-term teenager necessitates encompassing accurately comparable as inside stomach to adjust to the outside climate. Truth be told, warm blooded creatures enjoy assistance of presence homoeothermic, i.e., to partake an almost constant internal temperature level, accomplished independent of the ecological heat. Indispensable organs or elements of precocious children progress to the extremely smaller grade besides in method necessitates exceptional regard for familiarize to outer public of approaching temperature, mugginess, and light oxygen glassy. New-born teen has a few weaknesses as far as kind recommendation. A baby teen has a somewhat massive external region, destitute warm shield, and an inadequate amount of physique to go around as temperature bowl. The infant has slight volume to modest hotness by altering posture in addition to no volume to change the personal apparel in a response to warm weight. To contribute the similar climate as in the belly babies necessity be retained in a device known as hatchery. A baby hatchery is a gadget including of an unbending box similar nook inside this a newborn kid might be saved in a measured climate for clinical deliberation. A neonatal child hatchery stretches stable units of hotness, relative mugginess and oxygen focus the general humidity ought to follow set qualities as per number of brooding days. In this brilliant hatchery a baby`s information will be put away in cloud and we can check through by means of Phone or PC by specialist/guardian.

Neonatal Incubator, Premature Babies, IoT

[1] D. -A. Ivascu and M. -S. Munteanu, "Low-cost environmental monitoring system for incubators used in maternity hospitals," 9th International Conference on Modern Power Systems (MPS), 2021, pp. 1-6, 2021.
[2] K. M, K. S and S. R, "Infant Health Monitoring and Security System using IoT," Smart Technologies, Communication and Robotics (STCR), 2021, pp. 1-4, 2021.
[3] V. Govindaraj, A. Thiagarajan, S. Uthirapathi, A. S. Murugiah and D. D. Jeevagan, "Emergency Alert System for Neonatal Unit using IoT," 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021, pp. 700-703, 2021.
[4] E. Koscheeva, K. Slastnikov, A. Chupov and A. Konstantinova, "Non-Contact Temperature Mapping for Neonatal Intensive Care Unit," Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 2021, pp. 0060-0062, 2021.
[5] M.Shahib, M.Rashid, L.Hamawy, M.Arnout, I.ElMajzoub, A.J. Zaylaa ‘Advanced Portable Preterm Baby Incubator’- Fourth International Conference on Advances in Biomedical Engineering (ICABME), 2020.
[6] M Ali, M Abdelwahab et al., "Development of a Monitoring and Control System of Infant Incubator", International Conference on Computer Control Electrical and Electronics Engineering, pp. 1-4, 2020.
[7] Prof. Kranti, ‘Real Time Infants monitoring by developing an Embedded Device for incubator’- International Journal of Research in Computer and Communication Technology, Vol2, Issue 10, 2021.
[8] C. M. Hepps Keeney, C. S. Hung and T. M. Harrison, "Comparison of body temperature using digital infrared and tympanic thermometry in healthy ferrets (Mustela putorius furo)", Journal of Exotic Pet Medicine, vol. 36, pp. 16-21, January 2021.
[9] Abdul Saleem, Mohammed Junaid.M, Syeda Husna Mohammadi, Mohamed Jebran.P, Sarah Iram.L. Indikar, ‘Embedded Based Preemies Monitoring System with Jaundice Detection and Therapy’ - International Journal of Scientific & Technology Research Volume 2, Issue 6, 2020.
[10] Desai. M, ‘Design of an on stage incubator’- Bioengineering Conference (NEBEC) IEEE 37th Annual Northeast. 2020.
[11] M.Shahib, M.Rashid, L.Hamawy, M.Arnout, I.ElMajzoub, A.J. Zaylaa ‘Advanced Portable Preterm Baby Incubator’- Fourth International Conference on Advances in Biomedical Engineering (ICABME). 2020.
[12] Richard F, Guillermo G, William J, Danny M, Gabriel R ‘Low-Cost Neonatal Incubator’- Senior Design Project Report, Santa Clara University, California. 2020.

M. Dhanalakshmi, Poshitha M., "Real Time Monitoring of Premature Baby Using Arduino IOT," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.13-17, 2022.

The steganography has been used for long time before. The main use for it was for military and government messages, nowadays; the approaches of steganography become widely used for many purposes. Anyway, the researchers provide and found out many approaches while others enhanced the methods and the approaches of the steganography in order to improve the steganographic applications.Nowadays, the steganography application for sharing secure message used the multimedia files as a cover carrier for the secure message, since that many approaches has been proposed to use different type of the covers to send the secure message. The aim of this project is to use the methods of steganography using the video file as a cover carrier. The steganography is the art of protecting the information through embedding data in medium carrier. The video based steganography can be used as one video file. The use of the video based steganography can be more eligible than other multimedia files. As a result, the video based steganography the advantages of using the video file as a cover carrier for steganography have been proposed. In this project Steganography and encryption are bothused to ensure data confidentiality. However, the main difference between them is that with encryption anybody can see that both parties are communicating in secret. Steganography hides the existence of a secret message and in the best case nobody can see that both parties are communicating in secret.

Strganography, Data Embedding, Data Extraaction, Data Hiding, Data Security using Video Files

[1] A. Sarkar, U. Madhow, S. Chandrasekaran, and B. S. Manjunath, “Adaptive MPEG-2 video data hiding scheme,” in Proc. 9th SPIE Security Steganography Watermarking Multimedia Contents, pp. 373–376, 2007.
[2] K. Solanki, N. Jacobsen, U. Madhow, B. S. Manjunath, and S. Chandrasekaran, “Robust video-adaptive data hiding using erasure and error correction,” IEEE Trans. Video Process., vol. 13, no. 12, pp. 1627–1639, Dec. 2004.
[3] M. Schlauweg, D. Profrock, and E. Muller, “Correction of insertions and deletions in selective watermarking,” in Proc. IEEE Int. Conf. SITIS, pp. 277–284, Nov. – Dec. 2008.
[4] M. Wu, H. Yu, and B. Liu, “Data hiding in video and video: I. Fundamental issues and solutions,” IEEE Trans. Video Process., vol. 12, no. 6, pp. 685–695, Jun. 2003.
[5] M. Wu, H. Yu, and B. Liu, “Data hiding in video and video: II. Designs and applications,” IEEE Trans. Video Process., vol. 12, no. 6, pp. 696– 705, Jun. 2003.
[6] E. Esen and A. A. Alatan, “Forbidden zone data hiding,” in Proc. IEEE Int. Conf. Video Process., pp. 1393–1396, Oct. 2006.
[7] B. Chen and G. W. Wornell, “Quantization index modulation: A class of provably good methods for digital watermarking and information embedding,” IEEE Trans. Inform. Theory, vol. 47, no. 4, pp. 1423–1443, May 2001.
[8] D. Divsalar, H. Jin, and R. J. McEliece, “Coding theorems for turbo-like codes,” in Proc. 36th Allerton Conf. Commun. Control Comput., pp. 201–210. 1998.
[9] M. M. Mansour, “A turbo-decoding message-passing algorithm for sparse parity-check matrix codes,” IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4376–4392, Nov. 2006.
[10] Z. Wei and K. N. Ngan, “Spatio-temporal just noticeable distortion profile for grey scale video/video in DCT domain,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 3, pp. 337–346, Mar. 2009.
[11] M. Maes, T. Kalker, J. Haitsma, and G. Depovere, “Exploiting shift invariance to obtain a high payload in digital video watermarking,” in Proc. IEEE ICMCS, vol. 1. pp. 7–12. Jul. 1999.
[12] T. Kalker, G. Depovere, J. Haitsma, and M. J. Maes, “Video watermarking system for broadcast monitoring,” in Proc. SPIE Security Watermarking Multimedia Contents Conf., vol. 3657. pp. 103– 112, 1999.
[13] M. Maes, T. Kalker, J.-P. M. G. Linnartz, J. Talstra, F. G. Depovere, and J. Haitsma, “Digital watermarking for DVD video copy protection,” IEEE Signal Process. Mag., vol. 17, no. 5, pp. 47–57, Sep. 2000.
[14] K. Wong, K. Tanaka, K. Takagi, and Y. Nakajima, “Complete video quality-preserving data hiding,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 10, pp. 1499–1512, Oct. 2009.
[15] G. Tardos, “Optimal probabilistic fingerprint codes,” in Proc. 35th Annu. ACM STOC, pp. 116–125, 2003.

Narmatha K., R. Vadivel, "Robust Video Data Hiding Using Forbidden Zone Data Hiding and Selective Embedding," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.18-24, 2022.

Food loss is considered a problem because food loss refers to the loss of resources such as water, soil nutrition, and investment. Food shortages lead to food shortages. This means that poor people around the world are being deprived of food as the cost of available food is increasing. Providing fresh food is one of the major constraints which is already considered by various meal provider agents or companies. Many of them want to get an estimated number of stocks for given respective times, which could help them understand patterns and stocks required. Meal delivery companies want to know the estimated number of stocks that would be delivered or manufactured over the given period based upon previous data. Forecasting process is useful in various domains like weather forecasting, restaurants, retailing etc. It determines the expected demand for the future and establishes the level of readiness required on the supply side to meet the demand. This paper represents machine learning algorithms as an application to solve such problem with forecasting number of orders for given week and meal using algorithms Random Forest, XgBoost, Support Vector Machine, etc. with optimized results.

Machine Learning, Prediction, Random Forest, XgBoost, Support Vector Machines, Clustering

[1] D. Adebanjo and R. Mann, “Identifying problems in forecast-ing consumer demand within the fast paced commodity sector” Benchmarking: An International Journal, 7(3):223– 230, 2000.
[2] Md.Erfanul Hoque, Aerambamoorthy Thavaneswaran, Srimantoorao S. Appadoo, “A Novel Dynamic Demand Forecasting Model for Rresilient Supply Chains using Machine Learning.”,IEEE ISSN:0730-3157,2021
[3] K Siva Rama Krishna, Pooja Pasula, T.Kavyakeerthi, I.Karthik, “Identifying Demand Forecasting using Machine Learning for Business Intelligence.”, IEEE, ISBN:978-1-6654-1029-8, 2022
[4] Kenji Shinoda, Masato Yamada, Motoki Takanashi, Tetsuya Tsuboi, “Prediction of Restaurant Sales during high demand states using population statistical data.”, IEEE , ISBN:978-1-6654-2397-7, 2021

Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure, "Food Demand Forecasting Using Machine Learning And Statistical Analysis," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.25-29, 2022.

Destructive Tolerance Network (DTN) can drop packets received by selfish or malicious nodes. These routing malfunctions slow down packet delivery and waste system resources such as power and bandwidth. Techniques have been proposed to mitigate routing malfunctions in mobile ad hoc networks, but they cannot be applied directly to DTN due to the intermittent connections between the nodes. To address this issue, we propose a distributed method to detect packet drops on the DTN. In our scheme, the node should keep a signed contact record for the previous contact. Based on this, the next node to contact can determine if the node dropped the packet. Rogue nodes can falsely report contact records to avoid detection, so a small portion of each contact record is distributed to the specified number of watch nodes and the appropriate contact record. Can be collected to identify rogue nodes. We also propose a scheme to mitigate incorrect routing behavior by limiting the number of packets forwarded to malicious nodes. Trace-based simulations show that our solution is efficient and can effectively mitigate routing fraud.

Disruption Tolerant Networks, Detection, Mitigation, Routing Misbehaviour, Security

[1] W. Gao and G. Cao, “User-centric data dissemination in disruption tolerant networks,” in Proc. IEEE INFOCOM, pp. 3119–3127, 2011.
[2] H. Yang, J. Shu, X. Meng, and S. Lu, “Scan: Self-organized network-layer security in mobile ad hoc networks,” IEEE J. Sel. Areas Commun., vol. 24, no. 2, pp. 261–273, 2006.
[3] K. Fall, “Providing fault-tolerant ad-hoc routing service in adversarial environments,” in Proc. SIGCOMM, pp. 27–34, 2003.
[4] J. Burgess, B. Gallagher, D. Jensen, and B. Levine, “Maxprop: Routing for vehicle-based disruption-tolerant networks,” in Proc. IEEE INFOCOM, pp. 1–11, 2006.
[5] E. Daly and M. Haahr, “Social network analysis for routing in disconnected delay-tolerant manets,” in Proc. ACM MobiHoc, pp.32–40, 2007.
[6] V. Erramilli, A. Chaintreau,M. Crovella, and C. Diot, “Delegation forwarding,” in Proc. ACM MobiHoc, pp. 251–260, 2008.
[7] S. Marti, T. J. Giuli, K. Lai, and M. Baker, “Mitigating routing misbehaviorin mobile ad hoc networks,” in Proc. ACM MobiCom, pp.255–265, 2000.
[8] H. Yang, J. Shu, X. Meng, and S. Lu, “Scan: Self-organized network-layer security in mobile ad hoc networks,” IEEE J. Sel. AreasCommun., vol. 24, no. 2, pp. 261–273, 2006.
[9] K. Liu, J. Deng, P. K. Varshney, and K. Balakrishnan, “An acknowledgment-based approach for the detection of routing misbehavior inMANETs,” IEEE Trans. Mobile Comput., vol. 6, no. 5, pp. 536–550, May 2007.
[10] B. Burns, O. Brock, and B.N. Levine. “MV routing and capacity building in disruption tolerant networks”. In Proc. IEEE INFOCOM, pp.398– 408, March 2017.
[11] J. Davis, A. Fagg, and B.N. Levine. “Wearable Computers and Packet Transport Mechanisms in Highly Partitioned Ad hoc Networks”. In Proc. IEEE Intl. Symposium on Wearable Computers, pp.141–148, October 2018.
[12] J. Yang and C.-K. Lee Y. Chen, M. Ammar. ”Ferry Replacement Protocols In Sparse Manet Message Ferrying Systems”. In Proc. IEEE Wireless Communications and Networking (WCNC), March 2005.
[13] W. Zhao and M. Ammar. Message Ferrying: Proactive Routing In Highly-Partitioned Wireless Ad Hoc Networks. In Proc. IEEE Workshop on Future Trends in Distributed Computing Systems, May 2003.
[14] W. Zhao, M. Ammar, and E. Zegura. A Message Ferrying Approach for Data Delivery in Sparse Mobile Ad hoc Networks. In Proc. ACM Mobihoc, May 2004.
[15] W. Zhao, M. Ammar, and E. Zegura. Controlling the mobility of multiple data transport ferries in a delay-tolerant network. In IEEE INFOCOM, 2005.
[16]Yookesh, T. L., et al. "Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation." Materials today: Proceedings 47: 6101-6104, 2021.
[17]Kumar E. Boopathi, and V. Thiagarasu. "Segmentation using Fuzzy Membership Functions: An Approach." IJCSE, Vol.5, Issue.3, pp.101-105, 2017.

S. Devadharshini, R. Vadivel, "Mitigating Routing Misbehavior in Disruption Tolerant Networks," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.30-35, 2022.

The business model developed focused on expert system for health sector that uses intelligent agent to guide doctors in accurately carrying out disease control procedures. The objective of the paper is to create ontology-based data integration (OBDI) system process model that can uses intelligent agent to guide doctors in accurately carrying out disease control procedures. The system developed used to manage a disease registry that consists of the concepts of the domain, the attributes characterizing each disease, the different symptoms, and treatments. A model for enhanced medical intelligence process using ontology based technique developed. The design provided for a database system for storing medical records, software for enhanced Medical Intelligence Process that would be more user-friendly, flexible, adaptive, intelligent, agile and automatic in integrating and analyzing medical data thereby helping medical practitioners at various levels to make realistic intelligent and real-time decision on critical health issues. Object Oriented Analysis and Design Methodology (OOADM) adopted in the design of the system. The system achieved integration of various patients medical records from different hospitals using ontology based and virtual data integration technique that will allow clinic data of one patient collected together to form a combinational resource, and could be accessed by physician if authority is assigned to the physician. Ontology-based data integration technique for disease control procedure achieved 95% accuracy in predicting the disease control procedure.

Patients, production rule, OOADM, OBDI technique and Expert System

[1] Rosse, C. and Mejino, J.L.V. A reference ontology for biomedical informatics: the Foundational Model of Anatomy. Journal Biomedical Information, 3(6), 478-500, 2013.
[2] Helena, V. and Lidia, R. (2021) “Ontology-based Data Warehouse Development Process” Proceedings of the ITI 2009 31st International Conference. On Information Technology Interfaces, June 2225, 2009, Cavtat, Croatia, retrieved on June, 2021
[3] Vipul, K., Kei-Hoi, C., Donald, D., Matthias, S., Scott, M., Joanne, L., Susie, S., Ivan, H. and Raymond, H. (2008). Chapter 5 An Application Of Ontology-Based Data Integration For Biomedical Research” retrieved on May, 2016.
[4] Bostjan, G. & Vili, P. Automating ontology based information integration using service orientation” WSEAS Transactions on Computers Bostjan Grasic, Vili Podgorelec 9(6), 12-26, 2021.
[5] Marut, B. (2016). Ontology-based Clinical Reminder System to Support Chronic Disease Healthcare. Article in IEICE Transactions on Information and Systems. DOI: 10.1587/transinf.E94.D.432 · Source: DBLP. March 2016,
[6] Madhura, J., Dinithi, N., Daswin, S., Damminda, A., Brian, D., Kate, E .W. A data integration platform for patient-centered e-healthcare and clinical decision support. Research Center for Data Analytics and Cognition, La Trobe University, Victoria, Australia b School of Allied Health, La Trobe University. Victoria, Australia, 2020.
[7] Chih-Lin, C. Medical decision support systems based on machine learning. PhD (Doctor of Philosophy) thesis, University of Iowa, https://doi.org/10.17077/etd.o5gmwvxk, 2019.
[8] Sozan, S. M. Intelligent System for Identification Heart Diseases. A thesis submitted to the graduate school of applied sciences of near East University, 2019.
[9] Serdar, U. Intelligent Systems in Patient Monitoring and Therapy Management. Knowledge Systems Laboratory, Stanford University, 701 Welch Road Bldg. C Palo Alto, CA 94304, USA, 2014.
[10] Vishesh, V. Personal health record system and integration techniques with various electronic medical record systems. A Thesis Submitted to the Faculty of The College of Computer Science and Engineering in Partial Fulfillment of the requirements for the Degree of Master of Science Florida Atlantic University, Boca Raton, Florida, 2010.
[11] Dipti, P. S., Santosh, K.P. An Expert System for Diagnosis of Human Diseases. International Journal of Computer Applications, 1(13), 23-34, 2010.
[12] Soltan, R.A., Rashad, M. Z., El-Desouky, B. Diagnosis of Some Diseases in Medicine via computerized Experts System. International Journal of Computer Science & Information Technology, 5(5), 26-45, 2013.
[13] Ighoyota, B. A. and Sujatha, P. Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model. International journal Information Technology and Computer Science, 6(10), 13-28, 2017. Published Online October 2017 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2017.10.02
[14] Alexander, P and Joseph, L. (2017). Multi Agent Enhanced Business Intelligence for Localized Automatic Pricing in Grocery Chains” A dissertation submitted in fulfillment of the requirements of the degree of Doctor of Philosophy for the School of Information Technology, Bond University. December 2017.
[15] David, R., and Francis Real, J. An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. Journal of Biomedical Informatics, 45(3), 429-446, 2020.
[16] Augustina, B., Alejandra, C. and Nieves, R. B. (2014) “Ontology-Based Data Integration Methods: A Framework for Comparison”, retrieved on July, 2016.
[17] Bostjan, G. & Vili, P., (2010) “Automating ontology based information integration using service orientation” WSEAS Transactions on Computers Bostjan Grasic, Vili Podgorelec ISSN: 1109-2750 547, Vol.9, Issue 6, June 2010, retrieved on May, 2021
[18] Ali, Z., Ali, H., Osman, H. (2018) V-DIF: Virtual Data Integration Framework. International Journal of Computer Systems (ISSN: 2394-1065), Volume 05– Issue 05, May, 2018.
[19] Ali, Z. A Mapping Approach for Virtual Data Integration System Processes. International Journal of Advanced Computer Science and Applications, 9(12), 24-37, 2018.
[20] Vinoth, K. Ontology based Public Healthcare System in Internet of Things. 2nd International Symposium on Big Data and Cloud Computing (ISBCC’19), 2019.
[21] Taqdir, A., Maqbool, H., Wajahat. A., Muhammad, A., Jamil, H., Rahman, A., Waseem, H., Arif, J., Byeong, H., Sungyoung, L. Multi-model-based interactive authoring environment for creating shareable medical knowledge, Department of Computer Science and Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si 446-701, Gyeonggi-do, Republic of Korea, 2017.
[22] Richter, M.M., Weber, R.O. Case-based reasoning. Springer-Verlag Berlin, 2016.
[23] Jagannathan, R., Petrovic, S. Dealing with missing values in a clinical case-based reasoning system. In international conference on computer science and information technology (pp. 120-4). IEEE, 2009.
[24] Asma, A., A. Decision Tree Discovery for Diagnosis of Type II Diabetes.”, IEEE International Conference on Innovations in Information Technology (IIT), pp.303–307, 2011.
[25] Kapil, W., Mrudula , G. and Snehlata, D. “Decision Support System for Heart Disease Based on Support Vector Machine and Artificial Neural Network”, IEEE International Conference on Computer and Communication Technology (ICCCT),Vol.978,pp.741–745, 2010.
[26] Nassim, D., Elpiniki, I. P., Jos, D., Hans, C. and Marie-Christine, J., Clinical Decision Support System based on Fuzzy Cognitive Maps, INSERM UMR_S 872, Eq 20, Medicine Faculty, Pierre and Marie Curie University, Paris 6, France, 2014.

Asogwa E.C., Ejiofor V.E., Amanze B.C., Agbakwuru A.O., Belonwu T.S., "A Hybrid Model for Enhanced Medical Intelligence Process using Ontology Based and Virtual Data Integration Technique," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.36-42, 2022.

The constant flood of fresh patient data is causing problems in the healthcare system. Researchers have been utilizing this data to help the healthcare industry improve its capacity to manage major diseases. They are also looking at how patients might be informed of symptoms in a timely way, therefore avoiding the serious hazards that come with them. Diabetes is one such condition that is spreading at an alarming rate these days. It may lead to a number of significant problems, such as decreased eyesight, myopia, burning extremities, renal failure, and heart failure. When blood sugar levels rise over a certain threshold, the human body is unable to manufacture enough insulin to maintain the appropriate level. As a consequence, diabetics must be educated on the need of adhering to appropriate treatment regimens. As a consequence, early diabetes diagnosis and classification are crucial. This method employs Machine Learning approaches to improve diabetes prediction accuracy. Furthermore, the trials showed that ensemble classifier models outperformed base classifier models on their own. Its results were compared to the same dataset when various classification techniques such as random forest, support vector machine, decision tree, and naive bayes were applied to it.

ML, Diabetic Prediction, SVM, DT, ND, LR, Ensemble

[1] Adarsh, P & Jeyakumari, “Multiclass SVM-based automated diagnosis of diabetic retinopathy”, Communications and Signal Processing (ICCSP), International Conference on IEEE, pp. 206-210, D 2013.
[2] Akram, MU, Tariq, A, Khan, SA & Bazar, “Microaneurysm detection for early diagnosis of diabetic retinopathy”, Electronics, Computer and Computation (ICECCO), International Conference on IEEE, pp. 21-24, SA 2013.
[3] Amin, J, Sharif, M, Yasmin, M, Ali, H & Fernandes, “A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions”, Journal of Computational Science, vol. 19, pp. 153-164, SL 2017.
[4] Patil, BM, Joshi, RC & Toshniwal, “Association rule for classification of type-2 diabetic patients”, Machine Learning and Computing (ICMLC),Second International Conference on IEEE, pp. 330-334, , D 2010.
[5] Mansourypoor, F & Asadi, “Development of a reinforcement learning-based evolutionary fuzzy rule-based system for diabetes diagnoses”, Computers in Biology and Medicine, vol. 91, pp. 337-352, S 2017.
[6] Hayashi, Y & Yukita, “Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset”, Informatics in Medicine Unlocked, vol. 2, pp. 92-104, S 2016.
[7] Tomar, D, & Agarwal, “Hybrid feature selection based weighted least squares twin support vector machine approach for diagnosing breast cancer, hepatitis, and diabetes”, Advances in Artificial Neural Systems, vol. 2015, pp. 1-10, S 2015.
[8] Lukmanto, RB, Nugroho, A & Akbar, “Early detection of diabetes mellitus using feature selection and fuzzy support vector machine”, Procedia Computer Science, vol. 157, pp. 46-54, , H 2019.
[9] Vijayan, VV & Anjali, “Prediction and diagnosis of diabetes mellitus - A machine learning approach”, Intelligent Computational Systems (RAICS), 2015 IEEE Recent Advances in IEEE, pp. 122-127, C 2015.
[10] Y?ld?r?m, EG, Karahoca, A & Uçar, “Dosage planning for diabetes patients using data mining methods”, Procedia Computer Science, vol. 3, pp. 1374-1380, T 2011.
[11] Zarkogianni, K, Litsa, E, Mitsis, K, Wu, PY Kaddi, CD, Cheng, CW & Nikita, “A review of emerging technologies for the management of diabetes mellitus”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 12, pp. 2735-2749, KS 2015.
[12] Zhang, B Kumar, BV & Zhang, “Detecting diabetes mellitus and non proliferative diabetic retinopathy using tongue color, texture, and geometry features”, IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 491-501, D 2014.
[13] Yookesh, T. L., et al. "Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation." Materials today: Proceedings 47: 6101-6104, 2021.
[14] Kumar, E. Boopathi, and V. Thiagarasu. "Segmentation using Fuzzy Membership Functions: An Approach." IJCSE, ISSN: 2347-2693, 2017.

Pradeep Kumar G., R. Vadivel, "Python Based Diabetes Prediction Using Ensemble Machine Learning Techniques Using LR Algorithm and Hybrid Method," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.43-46, 2022.

: Covid19 is the menace of this century. World Health Organization (WHO) declared it pandemic in February, 2020. This RNA virus has catastrophic impact of the entire human civilization since it was initially reported to have been erupted from Wuhan, a city in Hubei province of China in late December 2019. In the first wave millions of people died in many countries. Even the developed countries like USA, France, Italy, United Kingdom etc. were in shock and could not prevent loss of human lives with their well-established medical infrastructure. Strict lockdown, quarantines were imposed. The hospitals were outnumbered by the severely ill patients who needed ventilation support. Many died without treatment, dead bodies were on the streets and mass graves became a practice. Developing and under-developed countries faced even more disastrous situations. Since then the virus is mutating and giving new challenges to human society in developing a cure. Until now RTPCR and other test are carried out to detect the disease. But they take somewhat longer time. So researchers are using artificial intelligence based techniques especially deep learning methods to develop new models using the CT scans (CTS) and chest X-ray (CXR) images of the patients to detect the disease in real time. This work focuses on the methods developed so far for detecting Covid-19 using convolutional neural network and compare their performances.

Covid-19, CT scan, Pneumonia, Chest X-ray, CNN.

[1] M. Ciotti, M. Ciccozzi, A.Terrinoni, W. Jiang, C. Wang, S. Bernardini,“The COVID-19 Pandemic”, Critical Reviews in Clinical Laboratory Sciences”, Vol.57, Issue. 6, pp.365-388, 2020.
[2] P. Zhou, X. Yang, X. Wang, B. Hu, L. Zhang,W Zhang ,S. HR, Y. Zhu, B. Li, C. Huang, H. Chen, J Chen, Y. Luo ,H Guo, R. Jiang, M. Liu, Y Chen, X. Shen, X Wang,X. Zheng, K. Zhao, Q. Chen,F. Deng, L. Liu, B. Yan, F. Zhan, Y. Wang, G. Xiao, Z. Shi, “A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin”, Nature. Vol.579, Issue.7798, pp.270-273,2020.
[3] F. Wu, S. Zhao,B. Yu, Y. Chen,W. Wang, Z. Song,Y. Hu ,Z. Tao, J. Tian, Y. Pei, M. Yuan, Y. Zhang, F. Dai, Y Liu, Q. Wang, J. Zheng, L. Xu, E. Holmes, Y. Zhang, “A New Coronavirus Associated With Human Respiratory Disease In China”, Nature. Vol.579, Issue.7798, pp. 265-269, 2020.
[4] J. Tregoning, K. Flight, S. Higham, Z. Wang, B. Pierce, “Progress of The COVID-19 Vaccine Effort: Viruses, Vaccines and Variants Versus Efficacy, Effectiveness And Escape”, Nature Reviews Immunology, Vol. 21,pp.626–636,2021.
[5] F. Almazán, I. Sola, S. Zuñiga, S. Marquez-Jurado, L. Morales, M. Becares, L. Enjuanes, “Coronavirus Reverse Genetic Systems: Infectious Clones and Replicons”, Virus Research, Vol.189,pp.262-270, 2014.
[6] G. Lippi, M. Plebani, “The Critical Role of Laboratory Medicine during Coronavirus Disease 2019 (COVID-19) And Other Viral Outbreaks”, Clinical Chemistry and Laboratory Medicine (CCLM), Vol.58, Issue.7, pp.1063-1069, 2020.
[7] K.Renu, P.L. Prasanna, A.V. Gopalakrishnan. “Coronaviruses Pathogenesis, Comorbidities and Multi-Organ Damage - A Review”, Life sciences Vol.255, pp. 117839, 2020.
[8] T.P. Mashamba-Thompson, E.D. Crayton, "Block chain and Artificial Intelligence Technology for Novel Coronavirus Disease 2019 Self-Testing", Diagnostics Vol.10, Issue.4, Issue.198,pp. 1-4,2020.
[9] Q. Hu, H. Guan, Z. Sun, L.Huang, C. Chen, T. Ai, Y. Pan, L. Xia, “Early CT Features and Temporal Lung Changes in COVID-19 Pneumonia in Wuhan, China”, European Journal of Radiology, Vol. 128,pp.109017,2020.
[10] D. Shen, G. Wu, H.I. Suk. “Deep learning in medical image analysis”. Annual Review of Biomedical Engineering, Vol. 19, pp. 221-248, 2017.
[11] A.M. Ismael, A. Sengür, “Deep Learning Approaches for COVID-19 Detection Based on Chest X-Ray Images”. Expert Systems with Applications Vol.164. Issue. 110054 pp.1-11, 2021.
[12] .H. Panwar, P.K. Gupta, M.K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, V. Singh, “A Deep Learning And Grad-CAM Based Color Visualization Approach For Fast Detection Of COVID-19 Cases Using Chest X-Ray And CT-Scan Images”, Chaos, Solitons & Fractals, Nonlinear Science, and Nonequilibrium and Complex Phenomena, Vol.140, Issue.110190, pp. 1-12,2020.
[13] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh,D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization", In the Proceedings of the IEEE International Conference on Computer Vision (ICCV),Venice, Italy, pp. 618-626,2017.
[14] J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, F.F.Li “Imagenet: A Large-Scale Hierarchical Image Database”. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2009), IEEE,Miami, USA, pp. 248–255, 2019.
[15] E. Hussain , M. Hasan, Md. A.Rahman ,I. Lee, T. Tamanna ,Md. Z. Parvez , “ Corodet: A Deep Learning Based Classification for COVID-19 Detection Using Chest X-Ray Images”, Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena, Vol.142, Issue.110495, pp. 1-12,2021.
[16] S. Chakraborty, B. Murali, A.K. Mitra, "An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images", International Journal of Environmental Research and Public Health, Vol.19, Issue.4, pp.1-12, 2013.
[17] V. Madaan, A. Roy, C. Gupta, P. Agarwal, A. Sharma, C. Bologa, R. Prodan, “XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks”, New Generation Computing Vol.39, pp.583–597,2021.
[18] T. Rahman, A. Khandakar, Y. Qiblawey, A.Tahir, S. Kiranyaz, S.B.A. Kashem, Md.T. Islam, S.A. Madeed, S.M. Zughaier, Md. S. Khan, Md. E.H. Chowdhury, “Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection Using Chest X-Ray Images", Computers in Biology and Medicine, Vol.132, Issue.104319, pp. 1-16, 2021.
[19] T. Ozturk, Md. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, U. R. Acharya, “Automated Detection Of COVID-19 Cases Using Deep Neural Networks With X-Ray Images”, Computers in Biology and Medicine, Vol. 121 ,Issue. 103792, pp.1-11, 2020.
[20] J.P. Cohen, P. Morrison, L. Dao, “COVID-19 Image Data Collection”, 2020. DOI.10.48550/arXiv.2003.11597.
[21] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, “Chestx-Ray8: Hospital Scale Chest X-Ray Database And Benchmarks On Weakly-Supervised Classification And Localization Of Common Thorax Diseases”, In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR2017),Honolulu, USA , pp. 3462-3471,2017.
[22] B.Nigam, A.Nigam, R. Jain, S.Dodia, N.Arora, B. Annapa, “COVID-19: Automatic Detection From X-Ray Images By Utilizing Deep Learning Methods”, Expert Systems With Applications, Vol. 176, Issue.114883, pp.1-11, 2021.
[23] Md. I. Khattak, M. Al-Hasan, A. Jan, N. Saleem, E. Verdu, N. Khurshid, “Automated Detection of COVID-19 using ChestX-Ray Images and CT Scans through Multilayer Spatial Convolutional Neural Networks”, International Journal of Interactive Multimedia and Artificial Intelligence, Vol.6, Issue.6, pp.15-24,2021.
[24] G. Gilanie, U.I. Bajwa, M.M. Waraich, M.M. Asghar, R. Kousar, A. Kashif, R.S. Aslam, M.M. Qasim, H. Rafique, “Coronavirus (COVID-19) Detection from Chest Radiology Images using Convolutional Neural Networks”, Biomedical Signal Processing and Control,Vol. 66, Issue.102490, pp. 1-6,2021.

Bibek Ranjan Ghosh, Siddhartha Banerjee, Ayush Nandi, Arya Panja, Pritam Mondal, Arunava Das, "Automated Covid-19 Detection System with CNN using Chest X-Ray and CT-Scans," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.47-52, 2022.

Recently, several data aggregation schemes based on privacy homomorphism encryption have been proposed and investigated on wireless sensor networks. These data aggregation schemes provide better security compared with traditional aggregation since cluster heads (aggregator) can directly aggregate the cipher texts without decryption; consequently, transmission overhead is reduced. However, the base station only retrieves the aggregated result, not individual data, which causes two problems. First, the usage of aggregation functions is constrained. For example, the base station cannot retrieve the maximum value of all sensing data if the aggregated result is the summation of sensing data. Second, the base station cannot confirm data integrity and authenticity via attaching message digests or signatures to each sensing sample. In this paper, we attempt to overcome the above two drawbacks. In our design, the base station can recover all sensing data even these data has been aggregated. This property is called “recoverable.” Experiment results demonstrate that the transmission overhead is still reduced even if our approach is recoverable on sensing data. Furthermore, the design has been generalized and adopted on both homogeneous and heterogeneous wireless sensor networks.

Concealed Data Aggregation, Wireless sensor networks, privacy homomorphism encryption

[1] R. Rajagopalan and P. Varshney, “Data-Aggregation Techniques in Sensor Networks: A Survey,” IEEE Comm. Surveys Tutorials, vol.8, no.4, pp.48-63, Oct.-Nov. 2006.
[2] S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “TAG:A Tiny Aggregation Service for Ad-Hoc Sensor Networks,”Proc. Fifth Symp. Operating Systems Design and Implementation,2002.
[3]J.-Y. Chen, G. Pandurangan, and D. Xu, “Robust Computation of Aggregates in Wireless Sensor Networks: Distributed Randomized Algorithms and Analysis,” IEEE Trans. Parallel Distributed Systems, vol.17, no.9, pp.987-1000, Sept.2006.
[4] H.C¸ am, S. O ¨ zdemir, P. Nair, D. Muthuavinashiappan, and H.Ozgur Sanli, “Energy-Efficient Secure Pattern Based Data Aggregation for Wireless Sensor Networks,” J. Computer Comm., vol.29, pp.446-455, 2006.
[5] H.Sanli, S. Ozdemir, and H. Cam, “SRDA: Secure Reference-Based Data Aggregation Protocol for Wireless Sensor Networks,”Proc. IEEE 60th Int’l Conf. Vehicular Technology (VTC ’04-Fall),vol.7, pp.4650-4654, Sep det. 2004.

Maharajan K., R. Vadivel, "Privacy Preserving Data Aggregation and Data Integrity in WSN," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.53-57, 2022.