Archive

Abstract:

Mobile Ad-Hoc Network is a decentralized organization that operates without a foundation. Due to the Mobile Ad-Hoc Network's self-configurable and simple organizational component, many applications may be run. Because of this, various applications are available. If helpful guidelines are established, Mobile Ad-Hoc Network will become dependable. We shall research the organization's competent steering convention for hypertext transfer protocol traffic. We must reach this conclusion with accuracy since this will be the main focus of our inquiry. Latency and throughput were employed to achieve show research goals. For this work reenactment inquiry, your expectations must be based on the conventions it chose since they performed better on all four perspectives. After analyzing its needs, an organization may improve its operations by choosing better conventions. This may boost an organization's efficiency. This research examined AODV, DSR, and OLSR routing methods. This study used OPNET Modeler 14.5 to enhance ad-hoc network performance.

Keywords:

AODV,DSR,Opnet,OLSR,Routing protocols,

Refference:

I. AbdulAmeer, Sabah Auda, et al. “Cyber Security Readiness in Iraq: Role of the Human Rights Activists.” International Journal of Cyber Criminology 16.2 (2022): 1-14.
II. Adhvaryu, K. ” Performance comparison of multicast routing protocols based on route discovery process for MANET ” . In Inventive Communication and Computational Technologies (pp. 79-85). Springer, Singapore, 2020.
III. Aina, F., Yousef, S., & Osanaiye, O. ” Analysing admission control for AODV and DSR routing protocol in mobile ad-hoc network” . Bulletin of Electrical Engineering and Informatics, Vol.10, No. 5, pp.2667-2677, 2021.‏
IV. Al Mojamed, M., & Kolberg, M. ” Structured Peer-to-Peer overlay deployment on MANET: A survey” . Computer Networks, Vol. 96, pp. 29-47, 2016.
V. Barbudhe, S. E., Thakare, V. M., & Sherekar, S. S. ” A Novel Approach to Minimize Flooding in MANET Using Symmetric and Asymmetric Paths “. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), Vol. 3, No.18‏ , 2014.
VI. Chitkara, M., & Ahmad, M. W.” Review on manet: characteristics, challenges, imperatives and routing protocols “. International journal of computer science and mobile computing, Vol. 3, No. 2, pp. 432-437., 2014. ‏
VII. Gupta, N. K., Yadav, R. S., & Nagaria, R. K. (2020). ” 3D geographical routing protocols in wireless ad hoc and sensor networks: An overview” Wireless Networks, Vol. 26, No. 4, pp.2549-256, 2020.
VIII. Hamdi, M. M., Audah, L., Rashid, S. A., Mohammed, A. H., Alani, S., & Mustafa, A. S. ” A review of applications, characteristics and challenges in vehicular ad hoc networks (VANETs)” . In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE., 2020.
IX. Hassan, M. H., Mostafa, S. A., Mohammed, M. A., Ibrahim, D. A., Khalaf, B. A., & Al-Khaleefa, A. S. ” Integrating African Buffalo optimization algorithm in AODV routing protocol for improving the QoS of MANET “. Journal of Southwest Jiaotong University, Vol. 54, No. 3, 2019.‏
X. Lavanya, P., Reddy, V. S. K., & Prasad, A. M. ” Performance comparison of DSDV, OLSR, AODV and DSR for mobile ad hoc networks” . International Journal of Emerging Technology and Advanced Engineering, Vol. 8, No. 1, pp. 209-218, 2018.
XI. Mezaal, Yaqeen S., et al. “Cloud computing investigation for cloud computer networks using cloudanalyst.” Journal of Theoretical and Applied Information Technology, 96(20), 2018.
XII. Padmavathy, N. ” Reliability Evaluation of Environmentally Affected Mobile Ad Hoc Wireless Networks”. Advances in Communication and Computational Technology, pp. 1297-1310, 2021.
XIII. Padmavathy, N. ” Reliability Evaluation of Environmentally Affected Mobile Ad Hoc Wireless Networks” . Advances in Communication and Computational Technology, pp. 1297-1310.‏
XIV. Quy, V. K., Nam, V. H., Linh, D. M., Ban, N. T., & Han, N. D. ” A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks” . Wireless Personal Communications, Vol. 120, No. 1, pp. 49-62, 2021. ‏
XV. Roshani, Saeed, et al. “Filtering power divider design using resonant LC branches for 5G low-band applications.” Sustainability 14.19 (2022): 12291.
XVI. Saini, T. K., & Sharma, S. C. ” Prominent unicast routing protocols for Mobile Ad hoc Networks: Criterion, classification, and key attributes” . Ad Hoc Networks, Vol. 89, pp. 58-77.‏, 2019.
XVII. Shareef, M. S., Mezaal, Y. S., Sultan, N. H., Khaleel, S. K., Al-Hillal, A. A., Saleh, H. M., … & Al-Majdi, K. (2023). Cloud of Things and fog computing in Iraq: Potential applications and sustainability. Heritage and Sustainable Development, 5(2), 339-350.
XVIII. Shantaf, A. M., Kurnaz, S., & Mohammed, A. H.” Performance evaluation of three mobile ad-hoc network routing protocols in different environments” . In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-6). IEEE, 2020.
XIX. Sharma, V., Alam, B., & Doja, M. N. ” An improvement in DSR routing protocol of MANETs using ANFIS ” . In Applications of Artificial Intelligence Techniques in Engineering (pp. 569-576). Springer, Singapore, 2019. ‏
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XXI. Soundarya, D., Janane, S., & Ramachandran, K. A. ” A Survey On DSR Routing Protocol. International Journal of Research in Engineering, Science and Management, Vol. 4, No. 2, pp.142-145, 2021.
‏
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XXIV. Tahir, A., Shah, N., Abid, S. A., Khan, W. Z., Bashir, A. K., & Zikria, Y. B. ” A three-dimensional clustered peer-to-peer overlay protocol for mobile ad hoc networks “. Computers & Electrical Engineering, Vol. 94, pp. 107364, 2021‏.
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XXVIII. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “New dual band dual-mode microstrip patch bandpass filter designs based on Sierpinski fractal geometry.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXIX. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “A novel design of two loosely coupled bandpass filters based on Hilbert-zz resonator with higher harmonic suppression.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXX. Yaqeen S. Mezaal, & Abdulkareem, S. F. (2018, May). New microstrip antenna based on quasi-fractal geometry for recent wireless systems. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
XXXI. Zanjireh, M. M., Shahrabi, A., & Larijani, H. ” Anch: A new clustering algorithm for wireless sensor networks “. In 2013 27th International Conference on Advanced Information Networking and Applications Workshops (pp. 450-455). IEEE, 2013.
XXXII. Zhang, J., Chen, T., Zhong, S., Wang, J., Zhang, W., Zuo, X., … & Hanzo, L. ” Aeronautical $ Ad~ Hoc $ networking for the Internet-above-the-clouds” . Proceedings of the IEEE, Vol. 107, No. 5, pp. 868-911, 2019.‏

UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT

Authors:

Prabhjot Kaur, Chander Kaur

DOI NO:

https://doi.org/10.26782/jmcms.2024.12.00012

Abstract:

Advancements in multimodal biometrics, which amalgamate multiple biometric traits, hold promise for augmenting the accuracy and robustness of biometric identification systems. The focal point of this innovative study is the enhancement of multimodal biometrics identification, using face and iris images as the key biometric traits. This work taps into the expansive collection of face and iris images present in the WVU-Multimodal dataset for evaluation purposes. Our proposed approach employs “Convolutional Neural Network (CNN)” architectures, notable for their efficacy in computer vision tasks, to extract potent discriminative features from the input images. This work specifically incorporates three popular CNN architectures: ResNet-50, InceptionNet, XceptionNet, and fine-tuned CNN. To amalgamate the extracted features, investigate various fusion techniques in the security-centric industry: early fusion, and score-level fusion. Early fusion is an approach that merges the raw images of both face and iris at the input level to a single CNN model. Use the Gabor approach to enhance the image's quality and make the face and iris information more visible. This technique modifies the histogram equalization process for local regions, thus enabling better visibility and subsequent feature extraction. Our experimental evaluation employs performance metrics like accuracy, “Equal Error Rate”, and “Receiver Operating Characteristic” curves. In this work undertakes a comparative analysis to appraise the performance of the different CNN architectures and fusion techniques under scrutiny.

Keywords:

CNN Models,Deep Learning,Gabor Technique,Security,Fusion,

Refference:

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NOVEL APPROACH FOR HYPERLEDGER FABRIC USING IOT FOR BANK TRANSACTIONS

Authors:

Haitham Al-Aboodi, Kheriolah Rahsepar Fard

DOI NO:

https://doi.org/10.26782/jmcms.2024.12.00013

Abstract:

The increase of attacks on bank accounts and credit cards through various types of attacks. Also, the rapid growth of online bank transfers and rapid transactions in stores and marketing worldwide make the urgent to use ciphering for securing the transactions process. In this paper, ways are proposed to enhance the algorithm that used ciphering and authentications of the user to ensure that the same user made the transactions. This is done through using the blockchain such as the Hyperledger fabric with the using the Internet of things for the authentications. The proposed algorithm helps in improving the safety of online transactions and helps protect the information of the user through the several nodes that use IOT for verifications and authentications. The results enhanced the blockchain of the Hyperledger fabric by enhancing the ways of the transactions and the process through the power of IoT.

Keywords:

Complex Network,Hyperledge Fabric,IoT,Online Transactions,

Refference:

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ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

Authors:

Bibhu Prasad Ganthia, Praveen B. M., S. R. Barkunan, A. V. G. A. Marthanda, N. M. G. Kumar, S. Kaliappan

DOI NO:

https://doi.org/10.26782/jmcms.2024.12.00014

Abstract:

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Keywords:

Battery Storage,Energy Management System,Microgrids,Monte Carlo Optimization,Optimization,Photovoltaic (PV),Uncertainties,Wind Energy,

Refference:

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COMPUTING THE INDEPENDENT DOMINATION METRIC DIMENSION PROBLEM OF SPECIFIC GRAPHS

Authors:

Basma Mohamed, Iqbal M. Batiha, Mohammad Odeh, Mohammed El-Meligy

DOI NO:

https://doi.org/10.26782/jmcms.2024.12.00015

Abstract:

We consider, in this paper, the NP-hard problem of finding the minimum independent domination metric dimension of graphs. A vertex set of a connected graph resolves if every vertex of is uniquely identified by its vector of distances to the vertices in . A resolving set of is independent if no two vertices in are adjacent. A resolving set is dominating if every vertex of that does not belong to is a neighbor to some vertices in . The cardinality of the smallest resolving set of , the cardinality of the minimal independent resolving set, and the cardinality of the minimal independent domination resolving set are the metric dimension of , independent metric dimension of , and the independent domination metric dimension of , respectively.

Keywords:

Dominant Metric Dimension,Domination Number,Independent Number,Metric Dimension,Resolving Dominating Set,

Refference:

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XII. I. M. Batiha, M. Amin, B. Mohamed, H. I. Jebril. : ‘Connected metric dimension of the class of ladder graphs’. Mathematical Models in Engineering. Vol. 10, pp. 65–74, 2024. 10.21595/mme.2024.23934
XIII. I. M. Batiha, N. Anakira, A. Hashim, B. Mohamed. : ‘A special graph for the connected metric dimension of graphs’. Mathematical Models in Engineering. Vol. 10, pp. 1–8, 2024. 10.21595/mme.2024.24176
XIV. I. M. Batiha, N. Anakira, B. Mohamed. : ‘Algorithm for finding domination resolving number of a graph’. Journal of Mechanics of Continua and Mathematical Sciences. Vol. 19, pp. 18–23, 2024. 10.26782/jmcms.2024.09.00003
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ENHANCING PVT SYSTEM PERFORMANCE WITH HYBRID Grp/Al₂O₃ NANOPARTICLES

Authors:

Zaid. A. Shaalan, Adnan. M. Hussein, M. Z. Abdullah

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00001

admin January 17, 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:

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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
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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
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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
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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
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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
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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

RELIABILITY AND ECONOMIC ANALYSIS OF A SYSTEM COMPRISING THREE UNITS I.E. OPERATIVE, HOT STANDBY AND WARM STANDBY

Authors:

L. Munda, G. Taneja, K. Sachdeva

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00002

admin January 17, 2025

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:

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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

ESMIoTHD: ENHANCED BLOCKCHAIN SECURITY AND MANAGEMENT FOR IOT-BASED HEALTHCARE DATA, A ROBUST FRAMEWORK FOR TRUST AND INTEGRITY

Authors:

Hari Prasad Chandika, Kontham Raja Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00003

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.
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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.

ENERGY-EFFICIENT SMART MEDICAL BRACELET FOR ALZHEIMER’S PATIENT MONITORING BASED WIRELESS COMMUNICATION SYSTEM

Authors:

Shahad Qassim Hadi, Mushtaq Ahmed Ali

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00004

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.

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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.

DETECTION OF NON-MELANOMA SKIN CANCER BY DEEP CONVOLUTIONAL NEURAL NETWORK AND STOCHASTIC GRADIENT DESCENT OPTIMIZATION ALGORITHM

Authors:

Premananda Sahu, Srikanta Kumar Mohapatra, Prakash Kumar Sarangi, Jayashree Mohanty, Pradeepta Kumar Sarangi

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00005

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
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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 .
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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 .
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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 .
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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 .

EXPLORING A NOVEL HEXACO PERSONALITY TRAITS ON TWITTER: AN ENSEMBLE-BASED NLP METHODOLOGY

Authors:

Tanvi Desai, Divyakant Meva

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00006

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

MACHINE LEARNING BASED PREDICTION AND INSIGHTS OF DIABETES DISEASE: PIMA INDIAN AND FRANKFURT DATASETS

Authors:

Mohammad Raquibul Hossain, Md. Jamal Hossain, Md. Mijanoor Rahman, Mohammad Manjur Alam

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00007

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,

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RANGAIG TRANSFORMS BASED HAM FOR SOLVING SOME TWO- DIMENSIONAL PDES

Authors:

Inderdeep Singh, Sandeep Sharma

DOI NO:

https://doi.org/10.26782/jmcms.2025.01.00008

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,

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