Journal Vol – 20 No -1, January 2025

Abstract:

The enhancement effect of hybrid nanofluids, especially with Grp/AL2O3 nanoparticles could be considered promising in enhancing the cooling of photovoltaic (PV) panels. Scholars have established that these nanoparticles improve heat transfer and convective heat transfer, therefore increasing the efficiency of solar panels. This work employed CFD analysis to investigate the characteristics of a new hybrid nanofluid, which is (Graphene Nanoplatelets (Grp) and aluminum Oxide (AL2O3). The system used in this study comprises three solar panels with identical specifications but using different cooling methods: air-cooled, water-cooled, and hybrid nanofluid-cooled. The found data demonstrates that the electrical efficiency of the solar cells, cooled by the hybrid nanofluid, is comparatively higher than the air-cooled and water-cooled solar cells: 12.2% and 7.6%, respectively, and the rise in power of the solar cells cooled by the hybrid nanofluid is comparatively higher to the air-cooled and water-cooled solar cells: 12.72% and 6.87 When applying the hybrid nanofluid cooling technique, the maximum surface temperature of the PV cells was reduced by 114% than that in air-cooled cells and 1.9% from water-cooled cells. As for the practical applications, it can be noted that hybrid nanofluids have demonstrated rather promising effects, enhancing the cooling efficacy of photovoltaic panels and, therefore, the efficacy of both overall solar energy systems.

Keywords:

photovoltaic (PV),hybrid nanofluid,electrical efficiency,power,CFD,

Refference:

I. A. M. Hussein, K. V. Sharma, R. A. Bakar, and K. Kadirgama. (2013). The effect of cross sectional area of tube on friction factor and heat transfer nanofluid turbulent flow. International Communications in Heat and Mass Transfer, 47, 49-55. 10.1016/j.icheatmasstransfer.2013.06.007
II. A.M. Hussein (2016). Adaptive Neuro-Fuzzy Inference System of friction factor and heat transfer nanofluid turbulent flow in a heated tube. Case Studies in Thermal Engineering, 8, 94-104. 10.1016/j.csite.2016.06.001
III. A.M. Hussein, K. V. Sharma, R. A. Bakar, K. Kadirgama (2014). A review of forced convection heat transfer enhancement and hydrodynamic characteristics of a nanofluid. Renewable and Sustainable Energy Reviews, 29, 734-743. 10.1016/j.rser.2015.12.256
IV. A.M. Hussein, K.V. Sharma, R.A. Bakar, K. Kadirgama. (2013). The effect of nanofluid volume concentration on heat transfer and friction factor inside a horizontal tube. Journal of Nanomaterials, 2013. 10.1155/2013/859563
V. A.M. Hussein, O.S. Khaleell, S.H. Danook. Enhancement of Double-Pipe Heat Exchanger Effectiveness by Using Water-CuO, NTU J. of Engineering and Technology, 1, (2022) 2 pp. 18-22. 10.56286/ntujet.v1i2.59
VI. A.M. Hussein, R.A. Bakar, K. Kadirgama, K.V. Sharma. Experimental measurement of nanofluids thermal properties. International Journal of Automotive and Mechanical Engineering, 7, pp.850-863. 10.1016/j.expthermflusci.2012.08.011
VII. A.M. Hussein. Thermal performance and thermal properties of hybrid nanofluid laminar flow in a double pipe heat exchanger, Exp. Therm. Fluid Sci., vol. 88, pp. 37–45, 2017. 10.1016/j.expthermflusci.2017.05.015
VIII. Afrand, M., & Ranjbarzadeh, R. (2020). Hybrid nanofluids preparation method. Hybrid Nanofluids for Convection Heat Transfer, 49–99. 10.1016/b978-0-12-819280-1.00002-1
IX. Ajiv, Alam, Khan., Mohd, Danish., Saeed, Rubaiee., Syed, Mohd, Yahya. (2022). Insight into the investigation of Fe3O4/SiO2 nanoparticles suspended aqueous nanofluids in hybrid photovoltaic/thermal system. Cleaner engineering and technology, 10.1016/j.clet.2022.100572
X. Al-ktranee M., Bencs P., (2020). Overview of the hybrid solar system. Analecta Technica Szegedinensia journal,Vol. 14, 1.100 -108, 10.2172/5839221
XI. Arora, N., Gupta, M., & Said, Z. (2022). Preparation and stability of hybrid nanofluids. Hybrid Nanofluids, 33–64. 10.1016/b978-0-323-85836-6.00002-8.
XII. AT Awad, AH Yaseen, AM Hussein, Evaluation of Heat Transfer and Fluid Dynamics across a Backward Facing Step for Mobile Cooling Applications Utilizing CNT Nanofluid in Laminar Conditions, CFD Letters 16 (10), 2024, 140-153. https://doi.org/10.37934/cfdl.16.10.140153
XIII. Ceylin, Şirin., Fatih, Selimefendigil., Hakan, F., Öztop. (2023). Performance Analysis and Identification of an Indirect Photovoltaic Thermal Dryer with Aluminum Oxide Nano-Embedded Thermal Energy Storage Modification. Sustainability. 10.3390/su15032422
XIV. Chavakula, R., Katari, N. K., Kadiyala, K. G., & Ramaswamy, G. (2022). Nanofluids: Basic information on preparation, stability, and applications. Smart Nanodevices for Point-of-Care Applications, 295–308. https://doi.org/10.1201/9781003157823-23.
XV. Che Sidik, N. A., Mahmud Jamil, M., Aziz Japar, W. M., & Muhammad Adamu, I. (2017). A review on preparation methods, stability and applications of hybrid nanofluids. Renewable and Sustainable Energy Reviews, 80, 1112–1122. 10.1016/j.rser.2017.05.221
XVI. Dhinesh Kumar, D., & Valan Arasu, A. (2018). A comprehensive review of preparation, characterization, properties and stability of hybrid nanofluids. Renewable and Sustainable Energy Reviews, 81, 1669–1689. 10.1016/j.rser.2017.05.257.
XVII. F. Zarda, A.M. Hussein, S.H. Danook, B. Mohamed. Enhancement of thermal efficiency of nanofluid flows in a flat solar collector using CFD, Diagnostyka 23 (4). 10.29354/diag/156384
XVIII. Giuseppina Ciulla, Valerio Lo Brano, Edoardo Moreci” Forecasting the Cell Temperature of PV Modules with an Adaptive System” 09 September 2013 10.1155/2013/192854
XIX. Giwa A, Yusuf A, Dindi A, Balogun HA. Polygeneration in desalination by photovoltaic thermal systems: a comprehensive review. Renew Sustain Energy Rev 2020; 130: 109946. 10.1016/j.rser.2020.109946
XX. Hamdallah, M. W., Jumaah, O. M., Shaalan, Z. A., & Hussein, A. M. (2021). Performance Enhancement of air conditioning (Split unit) using CUO/Oil Nano-Lubricant. Materials Science Forum, 1021, 97–106. 10.4028/www.scientific.net/msf.1021.97
XXI. Hongbing, Chen., Xuening, Gao., Congcong, Wang., Lizhi, Jia., Rui, Zhao., Junhui, Sun., Meibo, Xing., Pingjun, Nie. (2024). Experimental study on the performance enhancement of PV/T by adding graphene oxide in paraffin phase change material emulsions. Solar Energy Materials and Solar Cells, 10.1016/j.solmat.2023.112682
XXII. Jestin, Jose., Anurag, Shrivastava., Prem, Kumar, Soni., N., Hemalatha., Saad, Alshahrani., C., Ahamed, Saleel., Abhishek, Sharma., Saboor, Shaik., Ibrahim, M., Alarifi. (2023). An Analysis of the Effects of Nanofluid-Based Serpentine Tube Cooling Enhancement in Solar Photovoltaic Cells for Green Cities. Journal of Nanomaterials, 10.1155/2023/3456536
XXIII. K. Azeez, Z.A. Ibrahim, A.M. Hussein. Thermal Conductivity and Viscosity Measurement of ZnO Nanoparticles Dispersing in Various Base Fluids. J. of Adv. Res. in Fluid Mech. and Therm. Sci. 66, Issue 2 (2020) 1-10. 10.4028/www.scientific.net/amr.1101.344

XXIV. Mohammed Al-ktranee, Peter Bencs. (2020). Overview of the hybrid solar system. Analecta Technica Szegedinensia journal,Vol. 14, 1.100 -108, 10.14232/analecta.
XXV. Mosaad, R., Sharaby., M.M., Younes., Fawzy, Shaban, Abou-Taleb., Faisal, B., Baz. (2024). The influence of using MWCNT/ZnO-Water hybrid nanofluid on the thermal and electrical performance of a Photovoltaic/Thermal system. Applied Thermal Engineering, 10.1016/j.applthermaleng.2024.123332.
XXVI. Mrigendra, Singh., S.C, Solanki., Basant, Agrawal., Rajesh, Bhargava. (2023). Performance Evaluation of Photovoltaic Thermal Collector (PVT) by Cooling Using Nano Fluid in the Climate Condiation of India. Current World Environment, 10.12944/cwe.18.2.21
XXVII. NAKHAT, G. S., NILKHAN, V., & BARDE, R. V. (2021). Preparation, properties, stability and applications of Nanofluids: A Review. International Journal of Chemical and Physical Sciences, 10(5), 24. 10.30731/ijcps.10.5.2021.24-32
XXVIII. Omran Alshikhi Muhammet Kayfeci(2022). Experimental investigation of using graphene nanoplatelets and hybrid nanofluid as coolant in photovoltaic thermal systems. Thermal Science, 10.2298/tsci200524348a
XXIX. Padmaja, P., & Soni, H. (2019). Nanofluids: Preparation methods and challenges in stability. Nanofluids and Their Engineering Applications, 3–20. 10.1201/9780429468223-1.
XXX. Qamar, Fairuz, Zahmani., Norzelawati, Asmuin., Mariela, Sued., Samia, M., Mokhtar., Muhammad, Nazrul, Hakimi, Sahar. (2024). Nanofluid-Infused Microchannel Heat Sinks: Comparative Study of Al2O3, TiO2, and CuO to Optimized Thermal Efficiency. Journal of Advanced Research in Micro and Nano Engineering, 10.37934/armne.19.1.112
XXXI. Sandeep, Arya., Prerna, Mahajan. (2023). Introduction to Solar Cells. 10.1117/3.446028.ch1
XXXII. Sayan, Kumar, Nag., Tarun, Kumar, Gangopadhyay. (2023). Solar Photovoltaic Materials Development and Analysis. 10.22541/au.169038544.41077246/v1
XXXIII. Shivangi, Agarwal., Vinit, Sharma., Ajay, Kumar, Maurya., Pawan, Sen., Akanksha, Mishra. (2023). A Review of Solar Cells and their Applications. doi: 10.21467/proceedings.161.
XXXIV. T. Venkatesh, S. Manikandan, C. Selvam, S. Harish. Graphene nanofluids enhance solar PV/T system performance Improved heat transfer and electricity generation efficiency observed. 10.1016/j.icheatmasstransfer.2021.105794

XXXV. Talib, K., Murtadha. (2023). Effect of using Al2O3 / TiO2 hybrid nanofluids on improving the photovoltaic performance. Case Studies in Thermal Engineering, 10.1016/j.csite.2023.103112
XXXVI. Telugu, Venkatesh., S., Manikandan., C., Selvam., Sivasankaran, Harish., Sivasankaran, Harish. (2022). Performance enhancement of hybrid solar PV/T system with graphene based nanofluids. International Communications in Heat and Mass Transfer, 10.1016/j.icheatmasstransfer.2021.105794
XXXVII. Usman, Usman., Muhammad, Shoaib, Khan., Xiaowei, Zhu., Ahsan, Ali, Memon., Taseer, Muhammad. (2024). Investigating the Enhanced Cooling Performance of Ternary Hybrid Nanofluids in a Three-Dimensional Annulus-Type Photovoltaic Thermal System for Sustainable Energy Efficiency. Case Studies in Thermal Engineering, 10.1016/j.csite.2024.104700
XXXVIII. Wei Pang, Yanan Cui, Qian Zhang, Gregory.J. Wilson, Hui Yan. A comparative analysis on performances of flat plate photovoltaic/thermal collectors in view of operating media, structural designs, and climate conditions. Structural designs, and climate conditions. Renew Sustain Energy Rev 2020; 119: 10.1016/j.rser.2019.109599
XXXIX. Z.A. Ibrahim, Q.K. Jasim, A.M. Hussein, (2020). The impact of alumina nanoparticles suspended in water flowing in a flat solar collector. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 65(1), 1-12. 10.1016/0038-092x(61)90061-5
XL. Z.A. Shaalan, A.A. S, H.M. W, Heat pump performance enhancement by using a nanofluids (experimental study), J. Mech. Eng. Res. Develop. 44 (2) (2021), 01- 09. https://www.researchgate.net/publication/351690444
XLI. Z.H. Ali, A.M. Hussein, A review of enhancement of thermal performance of flat plate solar collectors through nanofluid implementation. Advances in Mechanical and Materials Engineering, 40(1), 2023, 139-148. 10.7862/rm.2023.14

View Download

Abstract:

The technique of redundancy, often known as standby, has been frequently used to increase system availability and reliability. The system comprises three units -operative, hot standby, and warm standby- which have varying failure rates following an exponential distribution. If the operative unit fails to function, a hot standby unit takes over if available. The switching of the warm standby unit concept is taken under consideration whenever a warm standby unit is required to be made operative. Warm standby unit repairs are prioritized when any unit has to be repaired since these repairs can be completed more quickly than other failed units as they work under less load and experience minimal failures. When both the operative and hot standby units fail, the hot standby is repaired first since it functions with the same load though it is not in operation, which allows for an earlier repair than the operative unit. Regeneration point technique has been used for finding various measures. Cut-off points for the revenue cost and cost per repairman visit have been computed to ascertain the profitability of the system.

Keywords:

Reliability,Regenerative Point Technique,Hot Standby,Warm Standby,Economical Analysis,

Refference:

I. A. Kumar, D. Pawar and S. C. Malik: ‘Profit analysis of a warm standby non-identical unit system with single server performing in normal/abnormal environment’. Life Cycle Reliability and Safety Engineering. Vol. 8, pp. 219-226, 2019. 10.13140/RG.2.2.15459.22567/7.
II. A. Manocha, G. Taneja and S. Singh: ‘Modeling and analysis of two- unit hot standby database system with random inspection of standby unit’. International Journal of Performability Engineering. Vol. 15, pp. 156-180, 2019. 10.1504/IJMOR.2019.10022964.
III. C. Christov, N. Stoytcheva: ‘Reliability and safety research of hot standby Microcomputer signally systems’. Periodicapolytechnic Ser- TRANSP. ENG. Vol. 27, pp. 17-28, 1999. 10.1016/0026-2714(93)90017.
IV. G. Mokaddis, M. El-Sherbeny and M. Ayid: ‘Stochastic Behaviours of a Two Unit Warm Standby System with Two Types of Repairmen and Patience Time’. Journal of Mathematics and Statistics. Vol. 5, pp 42-46, 2009. 10.1016/S0895-7177(98)00145-9.
V. L. I. Yuan, X. Y. Meng: ‘Reliability analysis of a warm standby repairable system with priority in use’. Applied Mathematics Modeling. Vol. 35, pp 4295-4303, 2011. 10.1016/j.apm.2011.03.002.
VI. L. Munda, G. Taneja: ‘Combined Redundancy Optimization for a System Comprising operative, Cold Standby and Warm Standby Unit’. Reliability: Theory & Application. Vol 18, pp 486-497, 2023. 10.24412/1932-2321.
VII. L. Munda, G. Taneja and K. Sachdeva: ‘Reliability and Profitability Analysis of a Mixed Redundancy Standby System Comprising Operative Unit Along with Hot and Cold Standby’. International journal Agriculture science. Vol 19, pp 1501-1509, 2023. 10.59467/IJASS.2023.19.1501
VIII. L. R. Goel, P. Gupta: ‘Analysis of a Two-Unit Hot Standby System with Three Modes’. Microelectronics reliability. Vol 23, pp 1029-1033, 1983. 10.1016/0026-2714(83)90515-2
IX. M. Saini, J. Yadav and A. Kumar: ‘Reliability, availability and maintainability analysis of hot standby’. Int J Syst. Assure Eng. Manage. Vol 13, pp 2458-2471, 2022. 10.1016/j.ress.2013.02.017.31.
X. Parveen, D. Singh and A. K. Taneja: ‘Redundancy Optimization for a System Comprising One Operative Unit and N Warm Standby Unit With Switching Time’. International journal Agriculture science. Vol 19, pp 1339- 1350, 2023. 10.59467/IJASS.2023.19.1339
XI. Parveen, D. Singh and A. K. Taneja: ‘Redundancy Optimization for a System Comprising One Operative Unit and N Hot Standby Unit’. Reliability theory & Application. Vol 4, pp 486-497, 2023. https://www.gnedenko.net/Journal/2023/042023/RTA_4_2023-46.pdf
XII. S. Batra, G. Taneja: ‘Optimization of number of hot standby units through reliability models for a system operative with one unit’. International Journal of Agricultural and Statistical Sciences. Vol 14, pp 0973–1903, 2018. https://connectjournals.com/file_html_pdf/2838401H_365-370a.pdf
XIII. S. Batra, G. Taneja: ‘Reliability modeling and optimization of the number of hot standby units in a system working with two operative units’. An international journal of advanced computer technology. Vol 10, pp 3059–3068, 2019. 10.26782/jmcms.2024.12.00010.
XIV. S. M. Rizwan, V. Khurana and G. Taneja: ‘Reliability modeling of a hot standby PLC system’. Proc. of International conference of Communication, Computer and power, Sultan Qaboos University, Oman, pp 486-489, 2005. 10.1080/02286203.2010.11442586.
XV. S. K. Srinivasan, R. Subramanian: ‘Reliability Analysis of a Three Unit Warm Standby Redundant System with Repair’. Annals of Operation Research, Vol. 143, pp 227-235, 2006. 10.1007.s10479-006-7384-z.
XVI. S. Kumari, R. Kumar: ‘Comparative Analysis of Two-Unit Hot Standby Hardware- Software Systems with Impact of Imperfect Fault Converges’. International Journal of Statistics and Systems, Vol. 12, pp 705-719, 2017. https://www.ripublication.com/ijss17/ijssv12n4_05.pdf

View Download

Abstract:

The primary focus of the study, entitled “Enhanced Blockchain Security and Management for IoT-Based Healthcare Data: A Robust Framework for Trust and Integrity”, was to determine the performance of four encryption algorithms, SADBTM, ECSBFQL, HBDMS, and ESMIoTHD, regarding their encryption and decryption times, average latency, and packet delivery ratio on different data sizes. It can be seen that employing blockchain security and management increases the security of IoT-based healthcare data. Consequently, this ensures that healthcare data can be linked to the confidentiality, integrity, and availability that remains central in today’s digitally interconnected world. Since the blockchain is a decentralized medical data-sharing tool that is impossible to alter, it is more secure due to guaranteed no potential unauthorized access to the data. Additionally, as indicated in the results during the performance evaluation of the blockchain systems in IoT healthcare networks, it was possible to improve patient data privacy, make data access easier, and ensure the trustworthiness of the healthcare systems. For instance, according to the study’s results, the encryption and decryption times had been computed in terms of milliseconds. The range of the data size was from 10 kilobytes to 100 kilobytes. When 100 data points had been encrypted, the ESMIoTHD had the lowest encryption time, namely, 12363 m, as compared to its encryption peers which include 13232 m, 13854 m, and 14376 m SADBTM, ECSBFQL, and HBDMS, respectively. Conversely, the results regarding the decryption times revealed a similar pattern to the encryption times. That is there was no significant deviation between the three encryption algorithms with the values being 13232 m, 13854 m, and 14376 m; however, ESMIoTHD had a decryption time of 13232 m. The average latency had been calculated in terms of milliseconds (m), whereby the results showed that ESMIoTHD has equivalent performance and its average latency was closest to its peers, such as 808 m for 100 data points. The packet delivery ratio had been computed in terms of percentages. Both the encryption and decryption algorithms had a similar pattern as they had been assessed based on the three results. However, the results show that ESMIoTHD had the highest PDR values in all data sizes, for example, the PDR was 98.896% for a 100 kb data size, which was far much higher than its peers. Based on the results of ESMIoTHD being the most efficient and reliable, particularly in terms of high throughput and low latency, these outcomes show that it is one of the leading encryption algorithms.

Keywords:

Average Latency,Blockchain Security,Data Encryption,Decryption Times,ESMIoTHD,Healthcare Data Privacy,IoT-based Healthcare,Packet Delivery Ratio (PDR),

Refference:

I. Agarwal, S., and Pal, R. “HierChain: A Hierarchical Blockchain-Based Framework for Secure Health Data Management.” IEEE Transactions on Industrial Informatics, vol. 15, no. 6, 2019, pp. 3286–3295. 10.1109/TII.2019.2904286.
II. Agbo, C. C., et al. “Blockchain Technology in Healthcare: A Systematic Review.” Healthcare, vol. 7, no. 2, 2019. 10.3390/healthcare7020056.
III. Boulos, M. N. K., et al. “How Blockchain Technology Can Transform the Healthcare Sector.” Journal of Medical Internet Research, vol. 20, no. 10, 2018. 10.2196/10326.
IV. Engelhardt, M. A. “Hitching Healthcare to the Blockchain: The Promise and the Pitfalls.” Applied Clinical Informatics, vol. 8, no. 3, 2017, pp. 452–465. 10.4338/ACI-2017-02-RA-0021.
V. Esposito, C., et al. “Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?” IEEE Cloud Computing, vol. 5, no. 1, 2018, pp. 31–37. 10.1109/MCC.2018.011791712.
VI. Gajendran, T., Saravanan, S., and Thangavel, M. “Integrated Elliptic Crypt with Secured Blockchain for Privacy-Preserving Federated Learning in IoMT.” IEEE Access, vol. 8, 2020, pp. 214832–214841. 10.1109/ACCESS.2020.3040148.
VII. Hölbl, M., et al. “A Systematic Review of the Use of Blockchain in Healthcare.” Symmetry, vol. 10, no. 10, 2018. 10.3390/sym10100470.
VIII. Kang, J., Yu, R., Huang, X., Maharjan, S., Zhang, Y., and Hossain, E. “Blockchain for Secure and Efficient Data Sharing in Healthcare: A Cross-Chain Empowered Federated Learning Framework.” IEEE Transactions on Industrial Informatics, vol. 16, no. 10, 2020, pp. 6309–6318. 10.1109/TII.2020.2971046.
IX. Kuo, T. T., et al. “Blockchain Distributed Ledger Technologies for Biomedical and Healthcare Applications.” Journal of the American Medical Informatics Association, vol. 24, no. 6, 2017, pp. 1211–1220. 10.1093/jamia/ocx068.
X. Li, J., Zhou, Y., and Wang, H. “Hybrid Blockchain Models for Scalable and Secure Healthcare Applications.” IEEE Transactions on Industrial Informatics, vol. 18, no. 2, 2022, pp. 1338–1347. 10.1109/TII.2021.3118395.
XI. Patel, D., and Kumar, V. “Lightweight Cryptographic Algorithms for IoT Healthcare Networks: Enhancing Efficiency and Security.” IEEE Internet of Things Journal, vol. 9, no. 3, 2022, pp. 2317–2327. 10.1109/JIOT.2021.3103901.
XII. Qathrady, M., Selvaraj, M., and Prabu, P. “A Dynamic Blockchain-Based Trust Management Model for Secure IoMT Networks.” IEEE Internet of Things Journal, vol. 7, no. 4, 2020, pp. 3054–3063. 10.1109/JIOT.2020.2968526.
XIII. Radanović, I., and Likić, R. “Opportunities for Use of Blockchain Technology in Medicine.” Applied Health Economics and Health Policy, vol. 16, no. 5, 2018, pp. 583–590. 10.1007/s40258-018-0412-8.
XIV. Roehrs, A., et al. “Personal Health Records: A Systematic Literature Review.” Journal of Medical Internet Research, vol. 19, no. 1, 2017. doi:10.2196/jmir.5876.
XV. Sharma, P., Gupta, R., and Verma, S. “Blockchain-Integrated Federated Learning System for Secure EHR Sharing During COVID-19 Pandemic.” Journal of Medical Internet Research, vol. 23, no. 5, 2021. 10.2196/24243.
XVI. Shuaib, M., et al. “Applications of Blockchain in Healthcare: Current Landscape and Challenges.” Arabian Journal for Science and Engineering, vol. 45, no. 4, 2020, pp. 3411–3431. 10.1007/s13369-020-04525-5.
XVII. Singh, A., Rao, K., and Mehta, N. “AI-Enhanced Blockchain Framework for Real-Time Data Analytics in IoT Healthcare Systems.” IEEE Access, vol. 11, 2023, pp. 3014–3025. 10.1109/ACCESS.2023.3247038.
XVIII. Wang, H., Song, Y., and Chen, X. “A Data Verifiable Access Control System for EHR Sharing Using Blockchain.” Journal of Medical Internet Research, vol. 23, no. 5, 2021. 10.2196/24521.
XIX. Zhuang, Y., et al. “Blockchain for IoT-Based Healthcare: The State-of-the-Art, Challenges, and Future Directions.” IEEE Internet of Things Journal, vol. 6, no. 5, 2019, pp. 7828–7855. 10.1109/JIOT.2019.2922038.

View Download

Abstract:

Power efficiency is a critical consideration in the design of wearable IoT devices, particularly in applications requiring continuous monitoring, such as systems for Alzheimer's patient care. The proposed system employs a hybrid approach to reduce power consumption by combining hardware and software optimization techniques. Regarding hardware, selected low-power, compact components, including the ESP32 microcontroller, the Max30102 sensor, and GPS. These components were chosen not only for their minimal energy requirements but also for their small size, which enhances the wearability and comfort of the device for extended periods. On the software side, we implemented power management strategies through the deep sleep mode of the ESP32 microcontroller, which significantly reduces power consumption by placing the device in a near-off state, with only a single GPIO pin remaining active to control peripheral power. By selectively powering down sensors during inactive periods, we effectively decrease the device's energy usage, thereby extending battery life. The combined hardware-software approach yielded substantial improvements in power efficiency. Based on the calculations, using a 350 mAh battery, a 30-second active period, and a 5-minute deep sleep interval, achieved an average current draw of approximately 9.16 mA, resulting in a battery life of around 38.2 hours. Compared to previous work in the field, this is a huge improvement. This optimized design allowed to development of a lightweight, wearable prototype capable of monitoring vital signs, tracking patient location, and providing medication reminders. Data is transmitted to the cloud, enabling caregivers to monitor the health metrics of patients in real-time remotely. By integrating hardware and software optimizations, our IoT solution offers a sustainable, practical means of improving both patient safety and quality of life while alleviating the caregiving burden through efficient, long-lasting wearable technology.

Keywords:

Energy-Efficient,The hybrid approach,IOT,Deep Sleep Mode,Esp32,Alzheimer’s disease,

Refference:

I. A. Pantelopoulos and N. G. Bourbakis, ‘‘A survey on wearable sensorbased systems for health monitoring and prognosis,’’ IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. 2010.

II. B. Al-Naami, H. Abu Owida, M. Abu Mallouh, F. Al-Naimat, M. Agha, and A.-R. Al-Hinnawi, “A new prototype of smart wearable monitoring system solution for Alzheimer’s patients,” Med. Devices Evid. Res., pp. 423–433, 2021.

III. D. Dakopoulos and N. G. Bourbakis, ‘‘Wearable obstacle avoidance electronic travel aids for blind: A survey,’’ IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 25–35, Jan. 2010.

IV. E. Sazonov, Wearable Sensors: Fundamentals, Implementation and Applications. Amsterdam, The Netherlands: Elsevier, 2014.

V. H. Sun, Z. Zhang, R. Q. Hu, and Y. Qian, ‘‘Wearable communications in 5G: Challenges and enabling technologies,’’ IEEE Veh. Technol. Mag., vol. 13, no. 3, pp. 100–109, Sep. 2018.

VI. J. Williamson, Q. Liu, F. Lu, W. Mohrman, K. Li, R. Dick, and L. Shang, ‘‘Data sensing and analysis: Challenges for wearables,’’ in Proc. 20th Asia South Pacific Design Autom. Conf., Jan. 2015, pp. 136–141.

VII. K. Hartman, Make: Wearable Electronics: Design, Prototype, and Wear Your Own Interactive Garments. Sebastopol, CA, USA: Maker Media, 2014.

VIII. O. D. Lara and M. A. Labrador, ‘‘A survey on human activity recognition using wearable sensors,’’ IEEE Commun. Surveys Tuts., vol. 15, no. 3, pp. 1192–1209, 3rd Quart., 2013.

IX. R. Ambika, S. M. Deekshitha, N. M. Keerthana, and K. Vandana, “Implementation of Wearable Device for Monitoring Alzheimer’s Patients,” in 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES), IEEE, 2023, pp. 1–6.

X. S. Seneviratne, Y. Hu, T. Nguyen, G. Lan, S. Khalifa, K. Thilakarathna, M. Hassan, and A. Seneviratne, ‘‘A survey of wearable devices and challenges,’’ IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2573–2620, 4th Quart., 2017.

XI. T. Rault, A. Bouabdallah, Y. Challal, and F. Marin, ‘‘A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications,’’ Pervasive Mobile Comput., vol. 37, pp. 23–44, Jun. 2017.

XII. X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao, ‘‘Incentives for mobile crowd sensing: A survey,’’ IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 54–67, 1st Quart., 2016.

View Download

Abstract:

Nowadays, people are doing a lot of work outside for a living. When they roam outside, there may be a chance to enter types of bacteria or fungi in our bodies through the skin by either the polluted gases from the vehicles or the ultraviolet rays emitted by the Sun. The expansion of skin problems for human beings has emerged as a significant problem, and the successful investigation has been observed as an arduous task for clinical experts or dermatologists. This paper has furnished an automatic diagnosis of skin cancer earlier with the help of deep learning techniques and the skin-related images captured by the Skin Biopsy test. In this approach, we detected non-melanoma using ensemble techniques related to deep convolutional neural networks and the stochastic gradient descent optimization technique. Furthermore, we used HAM 10000 as the data set for training and testing purposes, as well as the feature extraction technique Principal Component Analysis. This work also investigated a comparison of previous models. It was found that the proposed model gained an approximation of 98.57 % classification accuracy.

Keywords:

Skin Biopsy,Deep Convolutional Neural Network,Stochastic Gradient Descent,HAM 10000,Principal Component Analysis,

Refference:

I. Basha, SH Shabbeer, et al. “Impact of fully connected layers on the performance of convolutional neural networks for image classification.” Neurocomputing 378 (2020): 112-119. 10.1016/j.neucom.2019.10.008
II. Bayer, Markus, Marc-André Kaufhold, and Christian Reuter. “A survey on data augmentation for text classification.” ACM Computing Surveys 55.7 (2022): 1-39. 10.1145/3544558 .
III. Buchaiah, Sandaram, and Piyush Shakya. “Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection.” Measurement 188 (2022): 110506. 10.1016/j.measurement.2021.110506 .
IV. Daoud, Mohammad Sh, et al. “Gradient-based optimizer (gbo): a review, theory, variants, and applications.” Archives of Computational Methods in Engineering 30.4 (2023): 2431-2449. https://doi.org/10.1007/s11831-022-09872-y .
V. De Wet, Johann, et al. “An analysis of biopsies for suspected skin cancer at a tertiary care dermatology clinic in the Western Cape province of South Africa.” Journal of Skin Cancer 2020.1 (2020): 9061532. 10.1155/2020/9061532 .
VI. Dildar, Mehwish, et al. “Skin cancer detection: a review using deep learning techniques.” International journal of environmental research and public health 18.10 (2021): 5479. 10.3390/ijerph18105479 .
VII. Hasan, Basna Mohammed Salih, and Adnan Mohsin Abdulazeez. “A review of principal component analysis algorithm for dimensionality reduction.” Journal of Soft Computing and Data Mining 2.1 (2021): 20-30. https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/8032
VIII. Hossain, Sanoar, et al. “Fine-grained image analysis for facial expression recognition using deep convolutional neural networks with bilinear pooling.” Applied Soft Computing 134 (2023): 109997. 10.1016/j.asoc.2023.109997 .
IX. https://www.healthline.com/health/biopsy#risks.
X. https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000
XI. https://www.medicalnewstoday.com/articles/melanin.
XII. https://www.run.ai/guides/deep-learning-for-computer-vision/deep-convolutional-neural-networks/
XIII. Irfan, Tayyab, Abid Rauf, and M. Javed Iqbal. “Skin cancer prediction using deep learning techniques.” 2023 International Multi-disciplinary Conference in Emerging Research Trends (IMCERT). Vol. 1. IEEE, 2023. 10.1109/IMCERT57083.2023.10075313.
XIV. Khan, Asifullah, et al. “A survey of the recent architectures of deep convolutional neural networks.” Artificial intelligence review 53 (2020): 5455-5516. 10.1007/s10462-020-09825-6
XV. Kumar, R. Senthil, et al. “Skin cancer detection using deep learning.” 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. 10.1109/ICEARS53579.2022.9751826.
XVI. Laikova, Kateryna V., et al. “Advances in the understanding of skin cancer: ultraviolet radiation, mutations, and antisense oligonucleotides as anticancer drugs.” Molecules 24.8 (2019): 1516. 10.3390/molecules24081516 .
XVII. Lal, Sonal Tina, et al. “Changing trends of skin cancer: A tertiary care hospital study in Malwa region of Punjab.” Journal of Clinical and Diagnostic Research: JCDR 10.6 (2016): PC12. https://pmc.ncbi.nlm.nih.gov/articles/PMC4963704/
XVIII. Lim, Debbie, João F. Doriguello, and Patrick Rebentrost. “Quantum algorithm for robust optimization via stochastic-gradient online learning.” arXiv preprint arXiv: 2304.02262 (2023). https://arxiv.org/abs/2304.02262 .
XIX. Nahata, Hardik, and Satya P. Singh. “Deep learning solutions for skin cancer detection and diagnosis.” Machine learning with health care perspective: machine learning and healthcare (2020): 159-182. 10.1007/978-3-030-40850-3_8 .
XX. Naqvi, Maryam, et al. “Skin cancer detection using deep learning—a review.” Diagnostics 13.11 (2023): 1911. https://doi.org/10.3390/diagnostics13111911 .
XXI. Olayah, Fekry, et al. “AI techniques of dermoscopy image analysis for the early detection of skin lesions based on combined CNN features.” Diagnostics 13.7 (2023): 1314. https://doi.org/10.3390/diagnostics13071314 .
XXII. Rezvantalab, Amirreza, Habib Safigholi, and Somayeh Karimijeshni. “Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms.” arXiv preprint arXiv:1810.10348 (2018). https://arxiv.org/abs/1810.10348 .
XXIII. Sahu, Premananda, et al. “Detection and classification of Encephalon tumor using extreme learning machine learning algorithm based on Deep Learning Method.” Biologically Inspired Techniques in Many Criteria Decision Making: Proceedings of BITMDM 2021. Singapore: Springer Nature Singapore, 2022. 285-295. https://doi.org/10.1007/978-981-16-8739-6_26 .
XXIV. SM, Jaisakthi, et al. “Classification of skin cancer from dermoscopic images using deep neural network architectures.” Multimedia Tools and Applications 82.10 (2023): 15763-15778. https://doi.org/10.1007/s11042-022-13847-3 .
XXV. Sripada, Naresh Kumar, and B. Mohammed Ismail. “A multi-class skin cancer classification through deep learning.” Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2021. Singapore: Springer Singapore, 2022. 527-539. https://doi.org/10.1007/978-981-16-9605-3_36 .
XXVI. Stevenson, Paul, and Karl Rodins. “Improving diagnostic accuracy of skin biopsies.” Australian Journal of General Practice 47.4 (2018): 216-220. https://search.informit.org/doi/abs/10.3316/INFORMIT.487077259696069 .
XXVII. Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha. “Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN).” Diagnostic Pathology 17.1 (2022): 38. 10.1186/s13000-022-01189-5 .

View Download

Abstract:

Natural Language Processing (NLP) plays a crucial role in analyzing Twitter data to introduce an automated HEXACO model. Analyzing personality traits from social media data, particularly on platforms like Twitter, presents unique challenges due to the brevity, informal language, and rapid evolution of linguistic expressions. To overcome these drawbacks, this research presents a methodological framework for investigating a novel HEXACO personality trait using Twitter tweets. The HEXACO model encompasses Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience, offering a comprehensive basis for personality analysis. Our approach integrates advanced NLP techniques across key phases: preprocessing, feature extraction, feature selection, and final detection. Preprocessing involves tokenization, stop word removal, and stemming to standardize data quality. Feature extraction leverages contextual Term Frequency-Inverse Document Frequency (TF-IDF), and Global Vectors for Word Representation (GloVe) embeddings models to capture semantic and contextual information from tweets. Feature selection employs the Hybrid Kepler Inspired Secretary Bird (HKISP) algorithm, a combination of the Kepler Optimization Algorithm (KOA) and Secretary Bird Optimization (SBO). The final detection phase utilizes a weighted ensemble voting model comprising Artificial Neural Networks (ANN), Random Forest (RF), and k-Nearest Neighbours (k-NN) classifiers to enhance predictive accuracy and model robustness. The proposed technique achieved a classification Accuracy of 98.067% and a Hamming loss of 1.933%, which is proved to be superior to the existing models based on the obtained experimental findings.

Keywords:

NLP,Twitter tweets,HEXACO,Optimization,Hybrid Kepler Inspired Secretary Bird,weighted ensemble voting,

Refference:

I. Hassan, Saeed-Ul, Aneela Saleem, Saira Hanif Soroya, Iqra Safder, Sehrish Iqbal, Saqib Jamil, Faisal Bukhari, Naif Radi Aljohani, and Raheel Nawaz. : ‘Sentiment Analysis Of Tweets Through Altmetrics: A Machine Learning Approach.’ Journal of Information Science. Vol. 47, No. 6, pp. 712-726, 2021.
II. Khan, Rijwan, Piyush Shrivastava, Aashna Kapoor, Aditi Tiwari, and Abhyudaya Mittal. : ‘Social Media Analysis With AI: Sentiment Analysis Techniques For The Analysis Of Twitter Covid-19 Data.’ J. Crit. Rev. Vol. 7, No. 9, pp. 2761-2774, 2020.
III. Gupta, Vibhuti, and Rattikorn Hewett. : ‘Real-Time Tweet Analytics Using Hybrid Hashtags On Twitter Big Data Streams.’ Information. Vol. 11, No. 7, pp. 341, 2020.
IV. Wijeratne, Sanjaya, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, and Krishnaprasad Thirunarayan. : ‘Feature Engineering For Twitter-Based Applications.’ In Feature Engineering for Machine Learning and Data Analytics, CRC Press, pp. 359-393, 2018.
V. Ramírez-Sáyago, Ernesto. : ‘Sentiment Analysis From Twitter Data Regarding The Covid-19 Pandemic.’ 2020.
VI. AlBadani, Barakat, Ronghua Shi, and Jian Dong. : ‘A Novel Machine Learning Approach For Sentiment Analysis On Twitter Incorporating The Universal Language Model Fine-Tuning And SVM.’ Applied System Innovation, Vol. 5, No. 1, pp. 13, 2022.
VII. Anjum, Mehnaz, Akmal Khan, Shabir Hussain, M. Zeeshan Jhandir, Rafaqat Kazmi, Imran Sarwar Bajwa, and Muhammad Abid Ali. : ‘Sentiment Analysis of Twitter Tweets For Mobile Phone Brands.’ Pakistan Journal of Engineering and Technology, Vol. 4, No. 1, pp. 131-138, 2021.
VIII. Ramezani, Majid, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Meysam Asgari-Chenaghlu, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Mehrdad Ranjbar-Khadivi, Zoleikha Jahanbakhsh-Nagadeh, Elnaz Zafarani-Moattar, and Taymaz Akan. : ‘Automatic Personality Prediction: An Enhanced Method Using Ensemble Modelling.’ Neural Computing and Applications, Vol. 34, No. 21, pp. 18369-18389, 2022.
IX. Garg, Shruti, and Ashwani Garg. : ‘Comparison of Machine Learning Algorithms for Content-Based Personality Resolution of Tweets.’ Social Sciences & Humanities Open, Vol. 4, No. 1, pp. 100178, 2021.
X. Yang, Qi, Aleksandr Farseev, Sergey Nikolenko, and Andrey Filchenkov. : ‘Do We Behave Differently On Twitter And Facebook: Multi-View Social Network User Personality Profiling For Content Recommendation.” Frontiers in big Data, Vol. 5, pp. 931206, 2022.
XI. Salminen, Joni, Soon-gyo Jung, Hind Almerekhi, Erik Cambria, and Bernard Jansen. : ‘How Can Natural Language Processing And Generative Ai Address Grand Challenges Of Quantitative User Personas?’ In International Conference on Human-Computer Interaction, Cham: Springer Nature Switzerland, pp. 211-231, 2023.
XII. Balli, Cagla, Mehmet Serdar Guzel, Erkan Bostanci, and Alok Mishra. : ‘Sentimental Analysis Of Twitter Users From Turkish Content With Natural Language Processing.’ Computational Intelligence and Neuroscience, No. 1, pp. 2455160, 2022.
XIII. Alkhaldi, A. Nora, Yousef Asiri, Aisha M. Mashraqi, Hanan T. Halawani, Sayed Abdel-Khalek, and Romany F. Mansour. : ‘Leveraging Tweets For Artificial Intelligence Driven Sentiment Analysis On The Covid-19 Pandemic.’ In Healthcare, MDPI, Vol. 10, No. 5, pp. 910, 2022.
XIV. Hossny, Ahmad Hany, Lewis Mitchell, Nick Lothian, and Grant Osborne.: ‘Feature Selection Methods For Event Detection In Twitter: A Text Mining Approach.’ Social Network Analysis and Mining, Vol. 10, pp. 1-15, 2020.
XV. Alvarado, Berenice Jacqueline Sánchez, and Pedro Esteban Chavarrias Solano. ‘Detecting Disaster Tweets Using A Natural Language Processing Technique.’ Vol. 11, 2021.
XVI. Yang, Qi, Aleksandr Farseev, and Andrey Filchenkov. : ‘Two-Faced Humans On Twitter And Facebook: Harvesting Social Multimedia For Human Personality Profiling.’ In Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, pp. 39-47, 2021.
XVII. Klein, Z. Ari, Arjun Magge, Karen O’Connor, Jesus Ivan Flores Amaro, Davy Weissenbacher, and Graciela Gonzalez Hernandez. : ‘Toward Using Twitter For Tracking Covid-19: A Natural Language Processing Pipeline And Exploratory Data Set.’ Journal of medical Internet research, Vol. 23, No. 1, pp. e25314, 2021.
XVIII. Salsabila, Ghina Dwi, and Erwin Budi Setiawan. : ‘Semantic Approach For Big Five Personality Prediction On Twitter.’ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol. 5, No. 4, pp. 680-687, 2021.
XIX. KN, Pavan Kumar, and Marina L. Gavrilova. : ‘Latent Personality Traits Assessment From Social Network Activity Using Contextual Language Embedding.’ IEEE Transactions on Computational Social Systems, Vol. 9, No. 2, pp. 638-649, 2021.
XX. Yang, Yuan-Chi, Angel Xie, Sangmi Kim, Jessica Hair, Mohammed Al-Garadi, and Abeed Sarker. : ‘Automatic detection of twitter users who express chronic stress experiences via supervised machine learning and natural language processing.’ CIN: Computers, Informatics, Nursing, Vol. 41, No. 9, pp. 717-724, 2023.
XXI. Nanath, Krishnadas, and Geethu Joy. : ‘Leveraging Twitter Data To Analyze The Virality Of Covid-19 Tweets: A Text Mining Approach.’ Behaviour & Information Technology, Vol. 42, No. 2, pp. 196-214, 2023.
XXII. Dandash, Mokhaiber, and Masoud Asadpour. : ‘Personality Analysis For Social Media Users Using Arabic Language And Its Effect On Sentiment Analysis.’ arXiv preprint arXiv:2407.06314, 2024.
XXIII. Vysotska, Victoria, Petro Pukach, Vasyl Lytvyn, Dmytro Uhryn, Yuriy Ushenko, and Zhengbing Hu. : ‘Intelligent Analysis Of Ukrainian-Language Tweets For Public Opinion Research Based On Nlp Methods And Machine Learning Technology.’ International Journal of Modern Education and Computer Science (IJMECS), Vol. 15, No. 3, pp. 70-93, 2023.
XXIV. R. Patel, and K. Passi. : ‘Sentiment Analysis On Twitter Data Of World Cup Soccer Tournament Using Machine Learning.’ IoT, Vol. 1, No. 2, pp. 218–239, 2020.
XXV. Golam Mostafa, Ikhtiar Ahmed, and Masum Shah Junayed. : ‘Investigation Of Different Machine Learning Algorithms To Determine Human Sentiment Using Twitter Data.’ International Journal of Information Technology and Computer Science (IJITCS), Vol. 13, No. 2, pp. 38-48, 2021.
XXVI. Data Set Link: https://github.com/Tanvidesai-twitter/Twitter-Dataset.git

View Download

Abstract:

This paper focused on predicting diabetes disease using machine learning models which is a very active and highly important area of research. Six machine learning methods and three diabetes datasets were experimented with to investigate model performances. The methods are logistic regression, k-Nearest Neighbour, Gaussian Naïve Bayes, Decision Tree, Random Forest, and XGBoost. The datasets are Pima Indian, the Frankfurt Hospital dataset, and the combined dataset where all datasets have 08 (eight) feature variables and 01 (one) target variable. Train-test data split ratio can make a significant difference in model performance. Hence, two different split ratios 50-50 and 90-10 were experimented. Model performances were evaluated using four performance metrics which are precision, recall, F1-score, and accuracy. Random Forest and XGBoost were found to be highly efficient and best-performing among all the methods based on all performance metrics, all datasets, and both train-test split ratios. They performed comparatively better with the combined dataset which involved 2768 instances indicating the importance of a large dataset for better results. Also, the 90-10 train-test split ratio produced comparatively improved results than the 50-50 split ratio for all the datasets and even for almost all models.

Keywords:

Machine Learning Methods,Diabetes Prediction,Logistic Regression,Classification,Random Forest,XGBoost,

Refference:

I. Agliata, A., Giordano, D., Bardozzo, F., Bottiglieri, S., Facchiano, A., & Tagliaferri, R. (2023). Machine learning as a support for the diagnosis of type 2 diabetes. International Journal of Molecular Sciences, 24(7), 6775.
II. Aguilera-Venegas, G., López-Molina, A., Rojo-Martínez, G., & Galán-García, J. L. (2023). Comparing and tuning machine learning algorithms to predict type 2 diabetes mellitus. Journal of Computational and Applied Mathematics, 427, 115115.
III. Alenizi, A. S., & Al-karawi, K. A. (2023). Machine learning approach for diabetes prediction. International Congress on Information and Communication Technology, 745–756. Springer.
IV. Alzyoud, M., Alazaidah, R., Aljaidi, M., Samara, G., Qasem, M., Khalid, M., & Al-Shanableh, N. (2024). Diagnosing diabetes mellitus using machine learning techniques. International Journal of Data and Network Science, 8(1), 179–188.
V. Barik, S., Mohanty, S., Mohanty, S., & Singh, D. (2021). Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques. Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 2, 399–409. Springer.
VI. Chang, V., Bailey, J., Xu, Q. A., & Sun, Z. (2023). Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Computing and Applications, 35(22), 16157–16173.
VII. Ebrahim, O. A., & Derbew, G. (2023). Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021. Scientific Reports, 13(1), 7779.
VIII. Febrian, M. E., Ferdinan, F. X., Sendani, G. P., Suryanigrum, K. M., & Yunanda, R. (2023). Diabetes prediction using supervised machine learning. Procedia Computer Science, 216, 21–30.
IX. Gündoğdu, S. (2023). Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique. Multimedia Tools and Applications, 82(22), 34163–34181.
X. Hossain, M. R., & Ismail, M. T. (2020). Empirical mode decomposition based on theta method for forecasting daily stock price. Journal of Information and Communication Technology, 19(4), 533–558.
XI. Hossain, M. R., Ismail, M. T., & Hossain, M. J. (2022). Enhancing Stock Price Prediction Using Empirical Mode Decomposition, Rolling Forecast and Combining Statistical Methods. International Journal of Computing and Digital Systems, 12(1), 1343–1356.
XII. Ismail, L., Materwala, H., Tayefi, M., Ngo, P., & Karduck, A. P. (2022). Type 2 diabetes with artificial intelligence machine learning: methods and evaluation. Archives of Computational Methods in Engineering, 29(1), 313–333.
XIII. Khanam, J. J., & Foo, S. Y. (2021). A comparison of machine learning algorithms for diabetes prediction. ICT Express, 7(4), 432–439.
XIV. Kumar, N., Singh, P., Kumari, S., & Singh, B. K. (2023). Predicting Diabetes Using Machine Learning. 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 1737–1742. IEEE.
XV. Modak, S. K. S., & Jha, V. K. (2024). Diabetes prediction model using machine learning techniques. Multimedia Tools and Applications, 83(13), 38523–38549.
XVI. Naz, H., & Ahuja, S. (2020). Deep learning approach for diabetes prediction using PIMA Indian dataset. Journal of Diabetes & Metabolic Disorders, 19, 391–403.
XVII. Oikonomou, E. K., & Khera, R. (2023). Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology, 22(1), 259.
XVIII. Pima Indians Diabetes Database. https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database. Accessed 16 Oct. 2024.
XIX. Sakib, S., Yasmin, N., Tasawar, I. K., Aziz, A., Siddique, M. A. B., & Khan, M. M. R. (2021). Performance analysis of machine learning approaches in diabetes prediction. IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), 1–6. IEEE.
XX. Saxena, S., Mohapatra, D., Padhee, S., & Sahoo, G. K. (2023). Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms. Evolutionary Intelligence, 1–17.
XXI. Singh, Kabrambam. Type 2 Diabetes Dataset. IEEE, 17 Jan. 2024. ieee-dataport.org, https://ieee-dataport.org/documents/type-2-diabetes-dataset.
XXII. Srivastava, R., & Dwivedi, R. K. (2022). A survey on diabetes mellitus prediction using machine learning algorithms. ICT Systems and Sustainability: Proceedings of ICT4SD 2021, Volume 1, 473–480. Springer.
XXIII. Whig, P., Gupta, K., Jiwani, N., Jupalle, H., Kouser, S., & Alam, N. (2023). A novel method for diabetes classification and prediction with Pycaret. Microsystem Technologies, 29(10), 1479–1487.

View Download

Abstract:

This research presents a formulation of a hybrid scheme for the approximate solution of “Two-Dimensional Partial Differential Equations” (PDEs) used in engineering applications and several scientific. This approach combines the “Rangaig Transform with the Homotopy Analysis Method (HAM)” to form an efficient and robust explanation technique. Thus, the idea of our hybrid scheme combines the advantages of both approaches to make it easier to solve the given PDE while maintaining the highest accuracy. Several numerical examples have been solved to demonstrate the suggested method, and the outcomes make it evident how straightforward and accurate the method is for handling such a challenging issue.

Keywords:

Rangaig Transform,Homotopy Analysis Method,2D Telegraph Equation,2D Schrodinger Equation,2D Wave Equation,Test illustrations,

Refference:

I. Aboodh, K.S. (2013). The new integrale transform Aboodh transform. Global Journal of Pure and Applied Mathematics, 9(1), 35-43. https://www.researchgate.net/publication/286711380.
II. Alomari, A.K., Noorani, M.S.M. and Nazar, R. Explicit series solutions of some linear and nonlinear Schrodinger equations via the Homotopy analysis method, Communications in Nonlinear Science and Numerical Simulation. 2009, 14(4): 1196–1207. 10.1016/j.cnsns.2008.01.008.
III. Eltayeb, H. and Kilicman, A. A note on the Sumudu transforms and
Differential equations, Applied Mathematical Sciences. 2010, 4(22): 1089-1098. https://www.researchgate.net/publication/266468556.
IV. Elzaki, T.M. and Elzaki, S. M. Application of new transform “Elzaki Transform” to partial differential equations, Global Journal of Pure and Applied Mathematics. 2011, 1: 65-70. https://www.researchgate.net/publication/268179512.
V. Elzaki, T.M. The new integral transform “Elzaki Transform” Global Journal of Pure and Applied Mathematics. 2011, 1: 57-64. https://www.researchgate.net/publication/289123241.
VI. Ganjiani, M. Solution of nonlinear fractional differential equation using Homotopy Analysis method, Applied Mathematical Modeling. 2010, 34: 1634-1641. https://www.researchgate.net/publication/239344689.
VII. Gupta, V.G., & Kumar, P. (2015). Approximate solutions of fractional linear and nonlinear differential equations using Laplace homotopy analysis method. International Journal of Nonlinear Sciences, 19(2), 113-120. https://www.semanticscholar.org/paper/Approximate-Solutions-of-Fractional-Linear-and-Gupta-Kumar/cb3da27e4b5d064179668a3f1c37f97d62019741.
VIII. Jafari, H. and Seifi, S. Homotopy analysis method for solving linear and nonlinear fractional diffusion-wave equation. Comun. Nonlin. Sci. Num. Sim. 2009, 14(5): 2006-2012. https://www.researchgate.net/publication/222644128.
IX. Khan, Z.H., & Khan, W.A. (2008). N-transform properties and applications. NUST Journal of Engineering Science, 1, 127-133. https://www.researchgate.net/publication/216097528.
X. Khuri, S.A. A new approach to the cubic Schrodinger equation: an application of the decomposition technique, Applied Mathematics and Computation. 1998, 97: 251–254. 10.1016/S0096-3003(97)10147-3.
XI. Kilicman A. and ELtayeb, H. A note on Integral transform and partial differential equation, Applied Mathematical Sciences. 2010, 4(3):109-118. https://www.researchgate.net/publication/228359124.
XII. Liao, S.J. Beyond Perturbation: Introduction to the Homotopy Analysis Method, Chapman & Hall, CRC Press, Boca Raton, Fla, USA, 2003. https://www.researchgate.net/publication/259299701.
XIII. Liao, S.J. A new branch of solutions of boundary-layer flows over an impermeable stretched plate, International Journal of Heat and Mass Transfer. 2005, 48(12): 2529–2539. https://www.researchgate.net/publication/222430161.
XIV. Liao, S.J. Comparison between the homotopy analysis method and homotopy perturbation method, Appl. Math. Comput. 2005, 169:1186–1194. https://www.researchgate.net/publication/222698030.
XV. Liao, S.J. Notes on the homotopy analysis method: some definitions and theorems, Communications in Nonlinear Science and Numerical Simulation. 2009, 14(4): 983–997. https://www.researchgate.net/publication/223197156.
XVI. Liao, S.J. On the homotopy analysis method for nonlinear problems, Appl. Math. Comput.2004,147:499–513. https://www.researchgate.net/publication/222526491.
XVII. Maitama, S., & Zhao, W. (2019). New integral transform: Shehu transform a generalization of Sumudu and Laplace transform for solving differential equations. International Journal of Analysis and Applications, 17(2), 167-190. 10.28924/2291-8639-17-2019-167.
XVIII. Mansour, E. A., & Kuffi, E. A. (2022). Generalization of Rangaig transform. International Journal of Nonlinear Analysis and Applications, 13(1), 2227–2231. https://www.researchgate.net/publication/356839516.
XIX. Mohebbi, A. and Dehghan, M. The use of compact boundary value method for the solution of two-dimensional Schrodinger equation, Journal of Computational and Applied Mathematics, 2009, 225(1): 124–134. https://www.researchgate.net/publication/242980831.
XX. Rangaig, N., D. Minor, N., Fe I. Pe~nonal, G., Lord Dexter C. Filipinas, J., & C. Convicto, V. (2017). On Another Type of Transform Called Rangaig Transform. International Journal of Partial Differential Equations and Applications, 5(1), 42–48. 10.12691/ijpdea-5-1-6.
XXI. Rathore, S., Kumarb, D., Singh, J., & Gupta, S. (2012). Homotopy analysis Sumudu transform method for nonlinear equations. International Journal of Industrial Mathematics, 4(4), 301-314. https://www.researchgate.net/publication/304746982.
XXII. Sharma, S., & Singh, I. (2024a). Elzaki Transform Homotopy Analysis Techniques for Solving Fractional (2+1)-D and (3+1)-D Nonlinear Schrodinger Equations. In Communications on Applied Nonlinear Analysis (Vol. 31, Issue 6s). 10.52783/cana.v31.1224.
XXIII. Sharma, S., & Singh, I. (2024b). Mathematical Analysis of Some (3+1)-D Models via Rangaig Transform. In Communications on Applied Nonlinear Analysis (Vol. 32, Issue 3). 10.52783/cana.v32.1996.
XXIV. Singh, G., Singh, I.(2021), New hybrid technique for solving three dimensional telegraph equations, Advances in Differential Equations and Control Processes 24(2), 153-165. 10.17654/DE024020153.
XXV. Singh, G., Singh, I., AlDerea, A. M., Alanzi, A. M., & Khalifa, H. A. E. -W. (2023). Solutions of (2+1)-D & (3+1)-D Burgers Equations by New Laplace Variational Iteration Technique. Axioms, 12(7), 647. 10.3390/axioms12070647.
XXVI. Spiegel, M.R. (1965). Theory and Problems of Laplace Transform. Schaum’s Outline Series, New York: McGraw-Hill. 10.1007/978-0-387-28341-8_1.
XXVII. Ziane, D., & Cherif, M. H. (2022). the Homotopy Analysis Rangaig Transform Method for Nonlinear Partial Differential Equations. Journal of Applied Mathematics and Computational Mechanics, 21(2), 111–122. 10.17512/jamcm.2022.2.10

View Download