G. Prabhakaran, Rithik Balaji

Aircraft; Drone; Follow-Me Vehicle; Illumination; Marshalling; Ramp; Real-Time

The marshaller is human to control movement of aircraft by signalling, when aircraft moves out and moves in from ramp of an airport. This function is performed mainly on the ramp at an airport, occasionally on a taxiway and on the ramp of an airport. The Marshaller shall insure that they maintained the highest standards of preparedness in the safe ground operations of aircraft of all types and sizes. In our proposed research, we develop a marshalling drone equipped with a light panel piloted by the marshaller. This can be operated from any surrounding of Ramp area. The light panel will be mounted on top of the drone. It will have 3 separate square shaped illumination zones with one in the centre and the other 2 on the left and right side of the panel. These zones can illuminate independent of each other. The aim of this project is to create a symbiotic system for marshalling aircraft that is not entirely manual and not entirely automated either. This will create a highly reliable system. Normally to look at the marshallers, the pilots have to glance down, but with a drone, the pilot will be easily able to receive signals as the drone will be able to fly at eye level. Other applications of this drone could be its usage as a follow-me drone as opposed to the traditionally used follow-me vehicle and replace its operation. This project work is applied in the real time and obtained the results are showing excellent performance compare with real time marshalling. An upgradation regarding the technology and application will be provided to support future development in future research.

[1] Adeniran, Adetayo Olaniyi, Kanyio, Olufunto Adedotun, (2018). Artificial Intelligence in Aircraft Docking: The Fear of Reducing Ground Marshalling Jobs to Robots and Way-Out. Published in American International Journal of Multidisciplinary Scientific Research Vol. 1, No. 2, pp-25-31, (2018). DOI: 10.54655/aijmsr.v1n2p25
[2] Boaz Ben Moshe, Nir Shval, Jonathan Baadani, Itay Nagar, Harel Levy,(2012).Indoor Positioning and Navigation for micro UAV drones-work in progress. Published in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel.
[3] Brent Terwilliger, Dennis Vincenzi, David Ison, Kenneth Witcher, David Thirtyacre, and Adeel Khalid (2015). Influencing Factors for Use of Unmanned Aerial Systems in Support of Aviation Accident and Emergency Response. Published by Journal of Automation and Control Engineering Vol. 3, No. 3, June 2015
[4] Carlos Medina, Mayteé Zambrano and Kiara Navarro (2015). Led Based Visible Light Communication: Technology, Applications And Challenges – A Survey. Published by International Journal of Advances in Engineering & Technology, Aug., 2015.
[5] Chi Kwan Lee, Sinan Li, and S. Y. (Ron) Hui,(2011). A Design Methodology for Smart LED Lighting Systems Powered By Weakly Regulated Renewable Power Grids. Published by Ieee transactions on smart grid, vol. 2, no. 3, September 2011.
[6] Insung Hong, Jisung Byun, and Sehyun Park (2012). Intelligent LED Lighting System with Route Prediction Algorithm for Parking Garage. School of Electrical and Electronic Engineering, Chung-Ang University, Published by, the First International Conference on Intelligent Systems and Applications (2012).
[7] Insung Hong, Jisung Byun, and Sehyun Park (2012). Intelligent LED Lighting System with Route Prediction Algorithm for Parking Garage, Electrical and Electronics Engineering, Chung-Ang University. Published in The First International Conference on Intelligent Systems and Applications 2012.
[8] M. Naseeruddin, Samiya Khan (2016). Quadcoptor Robot for Autonomous Flight Missions in Populated and Unknown Area. Published by International Journal of Advanced Research Electrical Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified by the Organization) Vol. 5, Issue 6, June 2016.
[9] Tian Yongliang, Liu Hu, Yin Jiao, Luo Mingquang, Wu Guanghui, (2015). Evaluation Of Simulation –Based Training for Aircraft Carrier Marshalling with Learning Cubic And Kirkpatrick’s Models. Published in Chinese Journal of Aeronautics, (2015), Vol.28, Issue 1, pp-152-163. Url: http://dx.doi.org/ 10.1016 /j.cja.2014.12.002
[10] Tommaso Francesco Villa, Felipe Gonzalez, Branka Miljievic, Zoran D. Ristovski and Lidia Morawska, (2016). An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives. Published in Sensors MDPI journal, Vol 16, Issue 1072, pp-1-29, on 12 July 2016, 16, 1072; doi:10.3390/s16071072 www.mdpi.com/journal/sensors
[11] Wen-Tsai Sung and Jia-Syun Lin (2013). Design and Implementation of a Smart LED Lighting System Using a Self-Adaptive Weighted Data Fusion Algorithm. Published by Department of Electrical Engineering, National Chin-Yi University of Technology, Sensors 2013, 13, 16915-16939doi:10.3390/s131216915 Published: 6 December 2013.

J. Raji, Dr. P. Yamunaa, R.Tamilamuthan

Bidirectional Converter; ZVS Technique; Non Isolated Type

The bidirectional DC converter with ZVS technique is a topic of research interest in recent years. These type of converters may be either isolated with transformer or non-isolated type. This work studies the design of a non-isolated (transformer less) bidirectional DC to DC converter. Non-isolated converters are not as much of expensive as the isolated converters. They are ideally suited for aerospace applications. It also needs fewer switches and passive components. The objective is to construct a 200W bidirectional converter used to interface a low voltage (24V-30V) and a high voltage (200V) DC bus. The problem is formulated mathematically. The design is achieved through the formulas derived. The feasibility of design is validated through software simulation. Finally a prototype is fabricated and the performance is validated by an experimental setup

[1] Bordoloi, B. (2016). Curbing food wastage in a hungry world. BusinessLine, October
[2] Federicca Marra, Fighting Food Loss and Food Waste in Japan. (2013)
[3] Koivupalo, H., Hartikainen, H., Katajajuuri, J.-M., Silvennoinen, K., Heikintalo, N., Reinikainen,A. and Jalkanen, L. (2012). Influence of socio-demographic, behavioural and attitudinal factors on the amount of avoidable food waste generated in Finnish households.
[4] International Journal of Consumer Studies, 36(2), 183-191.
[5] Kumar, D. (2015). Problem of Food Wastage in India- Magnitude, Causes and Remedies. WordPress, June.
[6] Lazaros, Edward J, & Shackelford, Ray. (2008). Don’t Throw It Away! Raise Recycling Awareness Through Communications Project.Tech Directions, 67(6), 19-23
[7] Lyndhurst, B. (2007). Food behaviour consumer research:quantitative phase. Briefing paper, WRAP: UK
[8] Parfitt, J., Barthel, M., and Macnaughton, S. (2010). Food waste within food supply chains: quantification and potential for change to 2050. Philosophical Transactions of the Royal Society of Biological Sciences, 365(1554), 3065-3081.
[9] Quested, T., E., Parry,A.D., Easteal, S., and Swannell, R.(2013).Food and drink waste from households in the UK. Nutrition Bulletin, British Nutrition Foundation
[10] Sriraj, K. (2016). Tackling Food Wastage in India. The Pioneer, June 30.
[11] Wassermann, G., and Schneider, F. (2005). Edibles in Household Waste. Proceedings of the Tenth International Waste Management and Landfill Symposium, CISA, S.Margherita di Pula, Sardinia.
[12] WRAP (2008). Research into Consumer Behaviour in relation to food dates and portion sizes. Banbury: WRAP,October.
[13] WRAP (2010). A review of waste arisings in the supply of food and drink to UK households. Banbury:WRAP.
[14] WRAP (2009). Household Food and Drink Waste in the UK. Banbury:WRAP.
[15] WRAP (2007). We don´t waste food! A householder survey. Banbury:WRAP.
[16] Wenlock, R. W., Buss, D. H., Derry B. J., and Dixon, E.J.Responsible Consumption for Curbing Food Wastage: An Exploratory Enquiry (1979). Household food wastage in Britain. British Journal of Nutrition, 43(1), 53-70.
[17] Yang, Lei, Li, Zhen-Shan, & Fu, Hui-Zhen. (2011). Model of Municipal Solid Waste Source Separation Activity: A case Study of Beijing. Journal of the Air & Waste Management Association, 61(2), 157-163.

Rithanyaa S, Jayashree G , Nandhini S

Information Technology; Work-From-Home; Cloud Computing

The major goal of this paper is to provide in- depth knowledge on cloud computing technologies. IT organizations have been spending money to move their businesses to the cloud since the technology was first introduced. More than 45 percent of IT investment on system infrastructure, software infrastructure, and application software is expected to transition to cloud by 2024, according to estimates. In the wake of the COVID-19 pandemic, working from home has become the new standard for all employees. People may interact with clients, produce products, and collaborate with coworkers as if they were meeting and working in an office thanks to cloud computing. Many of today’s success stories may be traced back to cloud computing.

[1] Stefanie L, Böhm M, Riedl C, Krcmar H, “The Business Perspective of Cloud Computing: Actors, Roles and Value Networks”, 18th European Conference on Information Systems, ECIS 2010, Pretoria, South Africa, June 7-9, 2010.
[2] Sundee BK, “Cloud Computing for Business”, International Journal of Advances in Scientific Research and Engineering 2018.
[3] Baciu EI, “Advantages and disadvantages of cloud computing services, from the employee’s point of view”,
[4] National Strategies Observer No.2/Vol.1, 2015
[5] Buyya R, Vecchiola C, Thamarai SS, “Mastering Cloud Computing: Foundation and application programming”, ISBN – 978-0-12-411454-8, 2013.
[6] Bhopale SD, “Cloud Migration Benefits and Its Challenges Issue”, IOSR Journal of Computer Engineering (2013).
[7] Devasena LC, “Impact study of cloud computing on business development”, Operations Research and Applications: An International Journal (2014).

Sai Divya Kalagatla , Arun Sahayadhas

Password Authentication; Negative Password; Offline Attacks; Wireless Body Area Networks.

Although password authentication is still the most popular method of authentication, in spite of certain security vulnerabilities, secure password storage is an essential component of systems that rely on it. In this study, we present a framework for password authentication that can be readily integrated into current authentication systems and is intended for safe password storage. First, our framework uses a cryptographic hash algorithm (SHA-256) to hash the plain password that a client sends. Next, a negative password is created using the hashed password. Lastly, a symmetric-key method (AES) is used to encrypt the negative password into an encrypted negative password (ENP). Multi-iteration encryption may be used to increase security even further. It is challenging to decipher passwords from ENPs due to the symmetric encryption and cryptographic hash function. In addition, a given plain password has several associated ENPs, making pre-computation attacks (such as lookup table and rainbow table assaults) impractical. According to comparisons and studies of algorithm complexity, the ENP might withstand lookup table attacks and offer more robust password security against dictionary attacks. In addition to not adding additional components (salt), it is important to note that the ENP is still resistant to pre-computation attacks. Most notably, the ENP is the first password protection technique that just requires the plain password and combines the symmetric-key algorithm, the negative password, and the cryptographic hash function.

[1] Nazish Khalid a, Adnan Qayyum (2023) “Privacy-preserving artificial intelligence in healthcare: Techniques and applications” 158,106848
[2] Syed Jawad Hussain, Muhammad Irfan (2020) “Performance Enhancement in Wireless Body Area Networks with Secure Communication” https://doi.org/10.1007/s11277-020-07702-7
[3] Jehangir Arshad, Talha Ahmad Siddiqu (2023) “Deployment of an intelligent and secure cattle health monitoring system”, Egyptian Informatics Journal, Vol. 24, pp265-275.
[4] Jean-Paul A. Yaacoub a , Ola Salman (2020) “ Cyber-physical systems security: Limitations, issues and future trends”, Microprocessors and Microsystems. Vol. 77, 10320
[5] Victor Chang, Le Minh Thao Doan (2023) “Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices” , Journal of Business Research. Vol. 156, 113369
[6] Carmen Camara, Pedro Peris (2015) “Security and privacy issues in implantable medical devices: A comprehensive survey” Journal of Biomedical Informatics, Vol. 55, pp.272-289.
[7] Moustafa Mamdouh , Ali Ismail Awad (2021) “Authentication and Identity Management of IoHT Devices: Achievements, Challenges, and Future Directions” Computer & Security, 111, 102491
[8] Morteza Safaei Pour, Christelle Nader (2023) “A Comprehensive Survey of Recent Internet Measurement Techniques for Cyber Security” Computers & Security, 128, 03123
[9] Pablo Najera , JavierLopez (2011) “Real-time location and inpatient care systems based on passive RFID” Journal of Network and Computer Applications. Vol. 34, pp.980-989.
[10] Andrew J, Deva Priya Isravel (2023) “Blockchain for healthcare systems: Architecture, security challenges, trends and future directions”, Journal of Network and Computer Applications 215, 103633.
[11] Matan Kintzlingera,c, Nir Nissima (2019) “Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems, Journal of Biomedical Informatics Vol. 95, 103233
[12] Reyazur Rashid Irshad, Shahab Saquib Sohail (2023) “Towards enhancing security of IoT-Enabled healthcare system” Heliyon 9, e22336.
[13] Sushovan Chaudhury, Kartik Sau (2023) “A blockchain-enabled internet of medical things system for breast cancer detection in healthcare” Healthcare Analytics 4, 100221

M. Uma Shankari, S.Hareesh

Natural Language Processing; Machine Translation; Potential Ethical Issues.

The development of natural language processing (NLP) and machine translation (MT) systems necessitates careful consideration of their ethical and societal implications. The implementation of these technologies can have significant consequences for individuals, communities, and society as a whole. Therefore, it is imperative to conduct extensive research and analysis to identify potential ethical issues and formulate guidelines and best practices to address them. It is also essential to engage with stakeholders, including users, communities, and experts, to ensure that their perspectives and concerns are taken into account. By considering the ethical and societal implications of NLP and MT systems, we can ensure that these technologies are developed and deployed in a responsible manner that benefits society as a whole.

[1] Behera, R. K., Bala, P. K., Rana, N. P., & Irani, Z. (2023). Responsible natural language processing: A principlist framework for social benefits. Technological Forecasting and Social Change, 188, Article 122306. https://doi.org/10.1016/j.techfore. 2022. 122306.
[2] Qiu, X.; Sun, T.; Xu, Y.; Shao, Y.; Dai, N.; Huang, X. Pre-trained models for natural language processing: A survey. Sci. China Technol. Sci. 2020,63, 1872–1897.
[3] Howard, A.; Borenstein, J. Trust and Bias in Robots: These elements of artificial intelligence present ethical challenges, which scientists are trying to solve. Am. Sci. 2019,107, 86–90.
[4] Obermeyer, Z.; Powers, B.; Vogeli, C.; Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019,366, 447–453.
[5] Rodger, J.A.; Pendharkar, P.C. A field study of the impact of gender and user’s technical experience on the performance of voice-activated medical tracking application. Int. J. Hum. Compute. Stud. 2004,60, 529–544.
[6] Stubbs, M. Text and Corpus Analysis: Computer-Assisted Studies of Language and Culture; Blackwell: Oxford, UK, 1996.
[7] Jin, Z., & Mihalcea, R. (2022). Natural language processing for policymaking. In E. Bertoni, M. Fontana, L. Gabrielli, S. Signorelli, & M. Vespe (Eds.), Handbook of computational social science for policy (pp. 141-162). Springer.
[8] Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021). A survey on bias in deep NLP. Applied Sciences, 11(7), Article 3184.
[9] Hovy, D., & Prabhumoye, S. (2021). Five sources of bias in natural language processing. Language and Linguistics Compass, 15(8), Article e12432. https://doi.org/https://doi.org/10.1111/lnc3.12432
[10] Königs, P. (2022). Artificial intelligence and responsibility gaps: What is the problem? Ethics and Information Technology, 24(3), Article 36. https://doi.org/10.1007/s10676-022-09643-0
[11] Kreutzer, T., Vinck, P., Pham, P. N., An, A., Appel, L., DeLuca, E., Tang, G., Alzghool, M., Hachhethu, K., Morris, B., Walton-Ellery, S. L., Crowley, J., & Orbinski, J. (2020). Improving humanitarian needs assessments through natural language processing. IBM Journal of Research and Development, 64(1/2), 9:1-9:14. https://doi.org/10.1147/JRD.2019.2947014
[12] Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2021). Privacy and security issues in deep learning: A survey. IEEE Access, 9, 4566-4593. https://doi.org/ 10.1109/ACCESS.2020.3045078 Stanczak, K., & Augenstein, I. (2021). A survey on gender bias in natural language processing. arXiv preprint, arXiv:2112.14168.
[13] Tang, C. (2022). Privacy protection dilemma and improved algorithm construction based on deep learning in the era of artificial intelligence. Security and Communication Networks, 2022, Article 8711962. https://doi.org/10.1155/2022/8711962
[14] Xiao, Y., & Wang, W. Y. (2019). Quantifying uncertainties in natural language processing tasks. Proceedings of the AAAI conference on artificial intelligence, 33(1), 7322-7329. https://doi.org/10.1609/aaai.v33i01.33017322

Priya A, Aarthi S

Germinated Horse Gram Flour; Formulation; Nutrient Content; Popularization.

Aim: The aim of the study is to formulate and standardize germinated horse gram flour incorporated murukku. Objective: The objective of the study is to formulate and standardize germinated horse gram flour incorporated murukku, selection of most acceptable proportion, nutrient analysis, shelf life analysis of standard and the best product packed in polythene cover, cost analysis and popularization of the developed product among adolescent girls. Materials and methods: Horse gram was purchased from the local market, cleaned, soaked in water, drained the water and kept for germination. After germination horse gram was dried in sunlight and made up into flour and stored in an air tight container. The processed powder was incorporated in murukku by substituting the main ingredients at different levels such as 10%, 20%, 30% and 40% instead of main ingredient in the standard recipe. Using 9 point hedonic scale the best variation was selected. The selected best product and the standard were subjected to nutrient analysis, shelf life study, cost analysis and finally the product was popularized among adolescent girls. Results: The prepared products along with the standard were subjected to sensory analysis and most acceptable proportions are selected for shelf life study and nutrient analysis. From the study, it was concluded that the 10% germinated horse gram flour incorporated murukku was accepted when compared to other variations. The selected variation was analysed for nutrient content such as protein and iron. The protein in the selected variation (A) was 25.1/100gm whereas the protein content of standard is 15g/100g and iron content of selected sample 8.36g/100g and standard was 3.1mg/100g. The shelf life study shows that the prepared product is acceptable till 5th day without any microbial deterioration if it is stored in polythene cover properly. The cost of the prepared best product was slightly higher (Rs.100/-) than the standard (Rs.80/-). In the popularization study the entire participants accepted the product. Conclusion: The study concludes that the germinated horse gram flour which is a rarely used gram has high nutritional value and many health benefits which were unnoticed and the study creates the awareness to use pulse in any form to improve the health status.

[1] Dominick 2009 “real food for life”, volume II issue 1, Pp30-42.
[2] Jellinek,2008, “Tools of Nutrient Analysis”, International Journal of
[3] (Leo, 2007) “Automotive process based new product development” journal of food science and technology, volume4 issue 1 Pp450-460.
[4] Marimuthu,2015″Formulation,StandardizationandShelfLifeStudy”, InternationalresearchNutrientandFoodScience,Volume6,Issue4,Pp77-89.

N.Hinduja, S.Preethi, S.Sudhaa

Higher Education Institution; Fee management System; Cloud based ERP.

A higher education institution’s fee management system automates the collecting of student fees. The system manages tuition fees, project fees, and other payments for student services because it is an online platform. It can control budgets, payroll, other outlays, fees and offers total transparency and insight on fee-related transactions. All documentation is available online via the fee management system, making the procedure paperless. Alerts about upcoming fee payments can be sent through SMS or email to students and parents. The system has taken the place of all these time-consuming procedures. Parents may simply manage admissions for their kids with the aid of a system that includes integrated fee management system. Mobile phone fee management systems are very adaptable and it has no negative environmental effects. The system must be able to supply information to outside processes that need it for reporting. Such data paths ought to be present in an ERP system. The management of individual and general user accounts is made simple by the use of fee management system. Most importantly, the system makes sure that fees are paid on time, saving the time, money, effort, and resources on tracking fee payments.

[1] Yeboah Asuamah Samuel, Kumi Ernest, Cynthia Gyamfi, “Attitudes Towards Tuition Fees Payment in Tertiary Education: A Survey of Sunyani Polytechnic Marketing Students in Sunyani Ghana”, International Review of Management and Marketing, Vol. 2, No. 4, 2012, PP: 231-240.
[2] [Kartiki Datarkar, Neha Hajare, Nidhi Fulzele, Sonali Kawle, Vaibhav Suryavanshi, Dipeeka Radke, “Online College Management System”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.4, April- 2016, PP: 118-122.
[3] Nagavaram Kalyan,” Student Fee Management System”, International Journal of Research, Volume 04, Issue 13, October 2017, PP: 2491-2494.
[4] Vedant Banait, Vishal Murarkar, K. S. Chandwani, “Survey on Students Fees Management”, International Research Journal of Engineering and Technology, Volume 06 Issue 09, Sep 2019, PP: 1082-1084.
[5] Mary Nyondo, Nsama Lameck, “Design and Development of a Secondary School Payment System”, The International Journal of Multi-Disciplinary Research, 2020, PP: 1-23.
[6] Juhriyansyah Dalle, Dwi Hastuti, Taufik Rahman, A. Akrim, Sri Erliani,Taufik Hidayat, Siska Devina, Agustina Lestari, B. Baharuddin, Hesti Fibriasari, Akhmad Murjani, Erika Lismayani, Ahmad Yusuf, Candra Kusuma Negara, “Estimated Costs for Single Tuition Fee (STF) using Naïve Bayes Method”, Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) – Volume 1, PP: 280-289.
[7] Ajay Verma, Gaurav Gupta, Prince Kumar Sahu, “College Fees Payment System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 10, Issue 4, April 2021, PP: 246-250.
[8] E. Karunakar, K.Bhandhavya, L.Prathyusha, S.Vasanthi, “A Web Based Strategy on Enhancement of Student Fee Management System Using Web Development Technologies”, International Journal of Creative Research Thoughts, Volume 9, Issue 5 May 2021, PP: 563-567.
[9] Gunjankumbhar, Mansi Gaikwad, Pramilabhalerao, Sadanandfulari, “SECURE FEE MANAGEMENT SYSTEM”, International Research Journal of Modernization in Engineering Technology and Science, Volume 03, Issue 05, May-2021, PP 3201-3203
[10] Sudha L K, Yuktha Raju, Vidya G, Puneeth N, Yashaswini, “College Fee Management System using IoT”, International Journal of Engineering Research & Technology, Vol. 11 Issue 03, March-2022, PP:190-194.
[11] Deepak Kumar Verma , Vishal Pandey, Deep Sagar Agrahari, Anubhav Rai,” A Conceptual Framework for Fee Automation System”, International Journal of Research in Engineering and Science, Volume 10, Issue 7, July 2022, PP:925-928.
[12] Deepadharshini G, Keerthika R, Saranya S, Kavipriya S, Siva Ganga, “Fees Management System”, International Journal of Research Publication and Reviews, Vol 4, No 4, April 2023, PP: 2780-2781.

U. Gomathi, B. Ajmal Kapoor, M. Gunasekar, S. Madeshwaran, K. Ragul

Fire-Fighting Robot; Location Sharing; GSM; Temperature Detection.

The fire-fighting robot is used Fire in forest/hills areas are most dangerous one and it is uncontrollable also sometimes the information passed to the Fire station is too late about the trouble in the forest. Even by using the current technology, the fire was detected and informed to the fire station. But people around that place or any persons traveling in the nearby roadways didn’t get the information. By using the GSM, IOT-based Fire detection mechanism it will help to give the alert information to all. To control the fire in most dangerous and human can’t enterable region, by using the fire-fighting Robot it is easy to control that kind of fire also. the main scope of this project is to Detect the fire, Control the disaster, and preserve the life of both Forests and Animals

[1] Alhaza T, Alsadoon A, Alhusinan Z, Jarwali M, Alsaif K – New Concept For Indoor Fire Fighting Robot – 2018
[2] Tawfiqur Rakib, M. A. Rashid Sarkar- Design And Fabrication Of An Autonomous Fire Fighting Robot With Multisensor Fire Detection Using Pid Controller- 2018
[3] Dr. Ramkumar Prabhu, P. Deva- Development Of Fire Monitoring And Extinguishing Robot Using Iot – 2020
[4] Rajeshwarrao Arabelli, T. Bernatin- Self-Directed Fire Fighting Robot Using Internet Of Things And Machine Learning-2020
[5] K. Shamili Devi, K. Akhileswar, Ch. Vinayaka, M. Karthik, Y. K. Viswanadham- Fire Fighting Robot-2020
[6] S Kirubakaran, S P Rithanyaa, S P Thanavarsheni, E Vigneshkumar-Arduino Based Firefighting Robot-2021
[7] A R M Raafeek, N Satheeskanth, J Joy Mathavan, A Kunaraj- Iot Based Guided Fire Fighting Vehicle-2022.

D.Shobana, Vikramsai, Vishnupriya

Biodiversity Conservation; Artificial Intelligence; Systematic Conservation Planning; Conservation Strategies; Spatial Prioritization

Amid rising species extinction rates, we introduce a novel AI-based framework for spatial conservation prioritization. Leveraging reinforcement learning, it outperforms existing software by effectively balancing conservation costs and biodiversity benefits. This approach quantifies trade-offs and explores diverse metrics, consistently outshining simplistic methods. Empirical evidence demonstrates its efficacy in meeting conservation targets and generating interpretable prioritization maps. Integrating regular biodiversity monitoring enhances outcomes, offering a promising approach to optimize conservation efforts in a resource-constrained world.

[1] https://www.analyticsvidhya.com
[2] https://ieeexplore.ieee.org
[3] https://www.theguardian.com
[4] https://saiwa.ai/blog/ai-in-wildlife-conservation/
[5] https://medcraveonline.com/IJAWB/IJAWB-07-00192.pdf
[6] https://morungexpress.com/artificial-intelligence-ai-for-wildlife-conservation
[7] https://www.nature.com/articles/s41558-023-01769-3
[8] https://www.nature.com/articles/s41467-022-27980-y
[9] https://towardsdatascience.com/catching-poachers-with-machine-learning-118eec41d5b9
[10] https://www.analyticsvidhya.com/blog/2023/03/wildlife-conservation-through-ai/
[11] Artificial Intelligence and Conservation 2019 by Fei Fang. Book
[12] Our Planet Powered by AI: How We Use Artificial Intelligence to Create a Sustainable Future for Humanity 2023 by Mark Minevich. Book

Forecasting Cryptocurrency Trends using Web Application

Bitcoin; Ethereum; Litecoin; Cryptocurrency; Machine Learning; Price Prediction.

Global currency values have been declining, stock markets have been having a bad run, and investors have been losing capital over the past two years due to growing geopolitical and economic concerns. As a result, interest in virtual currencies has increased. One of the most well-known digital currencies, cryptocurrency, has gained attention from investors hoping to get a piece of it and from businesses accepting it as payment because of its consistent performance over the past several years. The study proposes a system aimed at accurately predicting the prices of Bitcoin, Ethereum, and Litecoin by considering various parameters influencing their values. For the first phase of investigation, aim to understand and identify daily trends in the cryptocurrency market while gaining insight into optimal features surrounding cryptocurrency price. The data set consists of various features relating to the Bitcoin, Ethereum and Litecoin price and payment network over the course of five years, recorded daily. For the second phase of investigation, using the available information to predict the sign of the daily price change with highest possible accuracy. The overall goal of the project is to construct a machine learning model that can predict price trends with results superior to that of random selection.

[1] B. I. Hameed, “Blockchain and Cryptocurrencies Technology: a survey”, JOIV : International Journal on Informatics Visualization, vol. 3, no. 4, Nov. 2019.
[2] Bo-Jhang Ho et al ,”Bitcoin Price Prediction Using Machine Learning: An Empirical Study”, 2020.
[3] E. Pintelas, I. Livieris, S. Stavroyiannis, T. Kotsilieris, and P. Pintelas, “Investigating the Problem of Cryptocurrency Price Prediction: A Deep Learning Approach”, 2020.
[4] Kim, Y.B., Lee, J., Park, N., Choo, J., et al.: “When Bitcoin encounters information in an online forum: Using text mining to analyses user opinions and predict value fluctuation”, PLOS One 12, e0177630, May 2017.
[5] Sebastian Kuhlmey et al,”Bitcoin Price Prediction Using Machine Learning: An Approach to Sample Size and Feature Set Optimization”, 2019.
[6] T. Phaladisailoed and T. Numnonda, “Machine learning models comparison for bitcoin price prediction” 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 506–511, 2018.
[7] Y. K. Pardeshi and P. P. Kale, “Technical analysis indicators in stock market using machine learning: A comparative analysis”, 12th
[8] International conference on computing communication and Networking Technologies (ICCCNT)20201
[9] Y. Liu, Z. Li, R. Nekhili, and J. Sultan, “Forecasting cryptocurrency returns with machine learning,” Research in International Business and Finance, vol. 64, p. 101905, Jan. 2023.
[10] Yufei Li et al , “A Comparative Study of Bitcoin Price Prediction Using Machine Learning”, (2019).
[11] [Z. Ai and Z. Yao, “The Investment Value and the Current Regulation of Cryptocurrencies Market Under the Confusion,” Jan. 2021.