Special Issue No. – 11, May 2024

Abstract:

The authors propose a stochastic analysis of a two-unit standby autoclave system with variable demand and inspection in this work. The autoclave system consists of a primary and a redundant unit, a dynamic demand profile, and an inspection mechanism to evaluate their condition. Inspection lowers the possibility of unanticipated failures and avoids financial loss. Best of our knowledge, many of the studies assume that the unit in cold standby mode is always reliable. This hypothesis lacks practical justification. Practically, its performance deteriorates due to environmental issues (dust, moisturizer, etc.). The authors found the same when visiting a ghee manufacturing plant in Punjab. Additionally, weather fluctuations also affect the production (as demand for ghee is higher in winter as compared to summer). Behaviour of redundant units is an intriguing aspect of this study and demonstrates its uniqueness. Thus, the authors explored two-unit standby autoclave systems subject to inspection on standby units with fluctuating demand. In the model, the main autoclave directly goes under repair when it fails, but the redundant autoclave undergoes inspection afterward beyond the determined redundant time, to check its possibility for repair or replacement. Replacement means a change of subparts, like Gear Box, etc., in an autoclave to put it in a working state instantly. Inspection adversely affects the system's reliability. Therefore, the statistical inference under the proposed innovation shows better results and a significant balance between the reliability and economy of the given stochastic system using the semi-Markov process (SMP) and regeneration point technique (RPT). By studying various scenarios about repair prices, inspection frequency, and fluctuating demand patterns, this research provides vital insights into the most effective approaches for handling redundant units and preserving system functionality.

Keywords:

Statistical Model,Stochastic Systems,Reliability,Availability,Manufacturing,Innovation,

Refference:

I. Bhardwaj, R. K., Komaldeep Kaur, and S. C. Malik. : ‘Stochastic modeling of a system with maintenance and replacement of standby subject to inspection’. American Journal of Theoretical and Applied Statistics. Vol. 4(5) pp. 339-346, (2015). 10.11648/j.ajtas.20150405.14
II. Gao Shan, and Jinting Wang. : ‘Reliability and availability analysis of a retrial system with mixed standbys and an unreliable repair facility’. Reliability Engineering & System Safety. Vol. 205, 107240, (2021). 10.1016/j.ress.2020.107240
III. Juybari, Mohammad N., Ali Zeinal Hamadani, and Mostafa Abouei Ardakan. : ‘Availability analysis and cost optimization of a repairable system with a mix of active and warm-standby components in a shock environment’. Reliability Engineering & System Safety. Vol. 237, 109375, (2023). 10.1016/j.ress.2023.109375
IV. Kakkar M. K., Bhatti J., Gupta G., Sharma K. D. : ‘Reliability analysis of a three unit redundant system under the inspection of a unit with correlated failure and repair times’. AIP Conf. Proc., 2357, 100025, (2022). 10.1063/5.0080964
V. Kakkar M. K., Bhatti J., Gupta G., : ‘Reliability Optimization of an Industrial Model Using the Chaotic Grey Wolf Optimization Algorithm’. In Manufacturing Technologies and Production Systems. CRC Press, pp. 317-324, (2023).
VI. Kamal, A., et al., : ‘Profit Analysis Study of Two-Dissimilar-Unit Warm Standby System under Different Weather Conditions’. J. Stat. Appl. Pro. Vol. 12(2), pp. 481-493, (2023). 10.18576/jsap/120213
VII. Levitin Gregory, Liudong Xing, and Yuanshun Dai. : ‘Cold standby systems with imperfect backup’. IEEE Transactions on Reliability. Vol. 65(4), pp. 1798-1809, (2015). 10.1109/TR.2015.2491599
VIII. Levitin Gregory, Liudong Xing, and Yanping Xiang. : ‘Optimizing preventive replacement schedule in standby systems with time consuming task transfers’. Reliability Engineering & System Safety. Vol. 205, 107227, (2021). doi.org/10.1016/j.ress.2020.107227
IX. Malhotra Reetu, and Gulshan Taneja. : ‘Stochastic analysis of a two-unit cold standby system wherein both units may become operative depending upon the demand’. Journal of Quality and Reliability Engineering. 896379, (2014). 10.1155/2014/896379
X. Malhotra Reetu, and Gulshan Taneja. : ‘Comparative study between a single unit system and a two-unit cold standby system with varying demand’. Springerplus. Vol. 4,pp. 1-17, (2015). 10.1186/s40064-015-1484-7
XI. Malhotra Reetu. : ‘Reliability and availability analysis of a standby system with activation time and varying demand’. Engineering Reliability and Risk Assessment. Elsevier. pp. 35-51, (2023). 10.1016/B978-0-323-91943-2.00004-6
XII. Malhotra Reetu, Faten S. Alamri, and Hamiden Abd El-Wahed Khalifa. : ‘Novel Analysis between Two-Unit Hot and Cold Standby Redundant Systems with Varied Demand’. Symmetry. Vol. 15.6, 1220, (2023). 10.3390/sym15061220
XIII. Malhotra Reetu, and Harpreet Kaur. : ‘Reliability of a Manufacturing Plant with Scheduled Maintenance, Inspection, and Varied Production’. Manufacturing Engineering and Materials Science. CRC Press, pp. 254-264, 2024.
XIV. Ram Mangey, Suraj Bhan Singh, and Vijay Vir Singh. : ‘Stochastic analysis of a standby system with waiting repair strategy’. IEEE Transactions on Systems, man, and cybernetics: Systems. Vol 43(3), pp. 698-707, (2013). 10.1109/TSMCA.2012.2217320
XV. Shekhar Chandra, Mahendra Devanda, and Suman Kaswan. : ‘Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays’. Quality and Reliability Engineering International. Vol. 39(7), pp. 3119-3139, (2023). 10.1002/qre.3421
XVI. Taneja Gulshan, Amit Goyal, And D. V. Singh. : ‘Reliability and cost-benefit analysis of a system comprising one big unit and two small identical units with priority for operation/repair to big unit’. MATHEMATICAL SCIENCES. Vol. 5(3), pp. 235-248, (2011). https://www.sid.ir/paper/322548/en
XVII. Wang Jinting, Nan Xie, and Nan Yang. : ‘Reliability analysis of a two-dissimilar-unit warm standby repairable system with priority in use’. Communications in Statistics-Theory and Methods. Vol. 50(4), pp. 792-814 (2021). 10.1080/03610926.2019.1642488
XVIII. Wang Jiantai, et al., : ‘Prognosis-driven reliability analysis and replacement policy optimization for two-phase continuous degradation’. Reliability Engineering & System Safety. Vol. 230, 108909, (2023). 10.1016/j.ress.2022.108909
XIX. Yang Dong-Yuh, and Chih-Lung Tsao. : ‘Reliability and availability analysis of standby systems with working vacations and retrial of failed components’. Reliability Engineering & System Safety. Vol. 182, pp. 46-55, (2019). 10.1016/j.ress.2018.09.020
XX. Yang Dong-Yuh, and Chia-Huang Wu. : ‘Evaluation of the availability and reliability of a standby repairable system incorporating imperfect switchovers and working breakdowns’. Reliability Engineering & System Safety. Vol. 207, 107366, (2021). 10.1016/j.ress.2020.107366
XXI. Zhang, Xin. : ‘Reliability analysis of a cold standby repairable system with repairman extra work’. Journal of systems science and complexity. Vol. 28(5), pp. 1015-1032, (2015). 10.1007/s11424-015-4081-5

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Abstract:

In today’s highly competitive world, the distribution of products plays a major role which makes it an important optimization problem related to determining the transportation route to transport a certain amount of products from supply points to demand points with minimum total transportation cost. This paper aims to introduce a new method to find the best and quick initial basic feasible solution for both balanced and unbalanced transportation problems. The proposed method always gives either optimal value or nearest to optimal value which is illustrated with two numerical illustrations i.e. one balanced and one unbalanced transportation problem. Also, the comparison of the results with some existing methods is also discussed.

Keywords:

Transportation Problems,Physical Distribution Problem,Optimal Solution,Initial Basic Feasible Solution,

Refference:

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VIII. G. Gupta, D. Rani, M. K. Kakkar. : ‘Optimal Solution of Fully Intuitionistic Fuzzy Transportation Problems: A Modified Approach, Manufacturing Technologies and Production Systems’. CRC Press. pp. 302-316, (2024), https://www.taylorfrancis.com/chapters/edit/10.1201/9781003367161-28/optimal-solution-fully-intuitionistic-fuzzy-transportation-problems-gourav-gupta-deepika-rani-mohit-kumar-kakkar
IX. G. Gupta, D. Rani and S. Singh. : ‘ An alternate for the initial basic feasible solution of category 1 uncertain transportation problems’. Proceedings of the National Academy of Sciences, India Section A, Vo. 90, pp. 157–167, (2019), http://dx.doi.org/10.1007/s40010-018-0557-8.
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XIII. J. Singla, G. Gupta, M. K. Kakkar and N. Garg. : ‘Revised Algorithm of Vogel’s Approximation Method (RA-VAM): An Approach to Find Basic Initial Feasible Solution of Transportation Problem’. ECS Transactions. Vol. 107(1), pp. 8757, (2022). http://dx.doi.org/10.1149/10701.8757ecst.
XIV. K. Karagul and Y. Sahin. : ‘A novel approximation method to obtain initial basic feasible solution of transportation problem’. Journal of King Saud University-Engineering Sciences. Vol. 32, pp. 211–218, (2020). http://dx.doi.org/10.1016/j.jksues.2019.03.003 .
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Abstract:

Skin malignancies are regarded as the most dangerous disease. Skin cancer has recently received much attention among people worldwide. An earlier diagnosis of skin cancer can lower the mortality rate. Skin cancer can be found and identified via dermoscopy. Automated tools using computer-aided diagnosis models become necessary because visually evaluating dermoscopic images is tedious and time-consuming. The healthcare industry has greatly benefited from recent machine learning advancements like deep learning. Modern technical designs and methodologies make detecting this type of cancer possible; however, automated classification in earlier phases is challenging due to the lack of contrast. As a result, a squeeze net algorithm-based automated computer system is developed for diagnosing skin illnesses. The HAM10000 dataset is gathered for skin lesions. Images of the four skin cancer conditions BCC, DF, MEL, BKL, and NV are included in the dataset. With a 92.25% overall accuracy, 85% precision, 84% recall, and 83% F1 score, the proposed dermonet model did well in classifying skin cancer conditions from the image samples.

Keywords:

skin lesions,squeeze net,classification,feature extraction,deep learning,

Refference:

I. A. Foahom Gouabou, J. Damoiseaux, J. Monnier, R. Iguernaissi, A. Moudafi, and D. Merad. : ‘Ensemble method of convolutional neural networks with directed acyclic graph using dermoscopic images: Melanoma detection application’. Sensors, Vo. 21(12), 3999, 2021. 10.3390/s21123999
II. A. Qureshi, and T. Roos. : ‘Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data sets’. Neural Processing Letters, Vol. 55(4), pp. 4461-4479, 2023. 10.1007/s11063-022-11049-4
III. B. Switzer, I. Puzanov, J. Skitzki, H. Hamad, and M. Ernstoff. : ‘Managing metastatic melanoma in 2022: a clinical review’. JCO Oncology Practice. Vol. 18(5), pp. 335-351, 2022. 10.1200/OP.21.0068
IV. C. Chang, W. Wang, F. Hsu, R. Chen, H. Chan. : ‘AI HAM 10000 Database to Assist Residents in Learning Differential Diagnosis of Skin Cancer’. IEEE 5th Eurasian Conference on Educational Innovation (ECEI). IEEE. pp. 1-3. 2022, February. 10.1109/ECEI53102.2022.9829465
V. C. Kaushal, S. Bhat, D. Koundal, and A. Singla. : ‘Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images’. Irbm Vol. 40(4), pp. 211-227, 2019. 10.1016/j.irbm.2019.06.001
VI. C. Scard, H. Aubert, M. Wargny, L. Martin, and S. Barbarot. : ‘Risk of melanoma in congenital melanocytic nevi of all sizes: A systematic review’. Journal of the European Academy of Dermatology and Venereology. Vol. 37(1), pp. 32-39, 2023. 10.1111/jdv.18581.
VII. F. Mallat, S.Matar, and B. Soutou. : ‘Umbilical seborrheic keratosis-like lesion developing after diode laser hair removal in an 18-year-old patient’. Journal of Cosmetic and Laser Therapy. Vol.25(1-4), pp. 54-56, 2023. 10.1080/14764172.2023.2241690.
VIII. J. Tembhurne, N. Hebbar, H. Patil, and T. Diwan T., : ‘Skin cancer detection using ensemble of machine learning and deep learning techniques’. Multimedia Tools and Applications. Vol. 82. pp. 27501-27524, 2023. 10.1007/s11042-023-14697-3
IX. L. Steele, X. Tan, L. Olabi, B. Gao, J. Tanaka, and H. Williams. : ‘Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: a systematic review’. Journal of the European Academy of Dermatology and Venereology. Vol. 37(4), pp. 657-665, 2023. 10.1111/jdv.18814
X. M. Alnowami. : ‘Very Deep Convolutional Networks for Skin Lesion Classification’. J. King Abdulaziz Univ. Eng. Sci. Vol. 30, pp. 43-54, 2019. 10.4197/Eng. 30-2.5
XI. M. Hu, Y.Li, Y, and X. Yang. : ‘Skinsam: Empowering skin cancer segmentation with segment anything model’. arXiv preprint arXiv: 2304.13973, 2023. 10.48550/arXiv.2304.13973
XII. M. Llamas-Velasco, T. Mentzel, E. Ovejero-Merino, E. M. Teresa Fernández-Figueras, and H. Kutzner. : ‘CD64 staining in dermatofibroma: A sensitive marker raising the question of the cell differentiation lineage of this neoplasm’. Journal of Molecular Pathology. Vol. 3(4), pp. 190-195, 2022. 10.3390/jmp3040016
XIII. N. Ceylan, S. Kaçar, E. Güney, and C. Bayilmiş. : ‘Detection of Grinding Burn Fault in Bearings by Squeeze Net’. 30th Signal Processing and Communications Applications Conference (SIU). IEEE. pp. 1-4, 2022, May. 10.1109/SIU55565.2022.9864895
XIV. N. Kumar and T. Sandhan. : ‘Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images’. National Conference on Communications (NCC). IEEE. pp. 1-5, 2023, February. 10.1109/NCC56989.2023.10068074
XV. N. Trivedi, V. Gautam, A. Anand, H. Aljahdali, S. Villar, D. Anand, and S. Kadry,: ‘Early detection and classification of tomato leaf disease using high-performance deep neural network’. Sensors. Vol. 21(23), 7987, 2021. 10.3390/s21237987
XVI. P. Dhiman, A. Kaur, Y. Hamid,E. Alabdulkreem, H. Elmannai, and N. Ababneh. : ‘Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing’. Sustainability. Vol. 15(5), 4576, 2023. 10.3390/su15054576
XVII. P. Dhiman, V. Kukreja, and A.Kaur. : ‘Citrus fruits classification and evaluation using deep convolution neural networks: an input layer resizing approach’. 2021 9th International Conference on Reliability, Infocom Technologies and Optimization, (Trends and Future Directions) (ICRITO). IEEE. pp. 1-4, 2021, September. 10.1109/ICRITO51393.2021.9596357
XVIII. R. Jain, S. Dubey, and G. Singhvi. : ‘The Hedgehog pathway and its inhibitors: Emerging therapeutic approaches for basal cell carcinoma’. Drug discovery today. Vol. 27(4), pp. 1176-1183, 2022. 10.1016/j.drudis.2021.12.005
XIX. S. Aggarwal,S. Gupta, A. Alhudhaif, D. Koundal, R. Gupta, and K. Polat. : ‘Automated COVID‐19 detection in chest X‐ray images using fine‐tuned deep learning architectures’. Expert Systems. Vol. 39(3), e12749, 2022. 10.1111/exsy.12749
XX. T. Mazhar, I. Haq, A. Ditta, S. Mohsan, F. Rehman, I. Zafar, and L. Goh, : ‘The role of machine learning and deep learning approaches for the detection of skin cancer’. Healthcare. MDPI. Vol. 11(3), 415, 2023, February. 10.3390/healthcare11030415
XXI. V. Anand, S. Gupta, A. Altameem, S. Nayak, R. Poonia, and A. Saudagar. : ‘An enhanced transfer learning based classification for diagnosis of skin cancer’. Diagnostics. Vol. 12(7), 1628, 2022. 10.3390/diagnostics12071628
XXII. V. Anand, S. Gupta, D. Koundal, S. Mahajan, A. Pandit, and A. Zaguia. ‘Deep learning based automated diagnosis of skin diseases using dermoscopy’. Computers, Materials & Continua. Vol. 71(2), pp. 3145-3160, 2022. 10.32604/cmc.2022.022788
XXIII. W. Gouda, N. Sama, G. Al-Waakid, M. Humayun, and N. Jhanjhi. : ‘Detection of skin cancer based on skin lesion images using deep learning’. Healthcare. MDPI. Vol. 10(7), 1183, 2022, June. DOI:10.3390/healthcare10071183.
XXIV. Y. Dahdouh, A. Anouar Boudhir, and M. Ben Ahmed. : ‘A New Approach using Deep Learning and Reinforcement Learning in HealthCare: Skin Cancer Classification’. International journal of electrical and computer engineering systems. Vol. 14(5), pp. 557-564, 2023. 10.32985/ijeces.14.5.7

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Abstract:

The healthcare sector is characterized by a vast amount of information and holds significant potential for improvement through the integration of state-of-the-art technologies. Deep learning models have been regarded as being particularly ideal since they can efficiently handle and analyze enormous amounts of data, allowing them to attain the highest possible level of accuracy. This study aims to conduct a comprehensive analysis of various deep learning models by comparing their performance on different datasets. Additionally, it will focus on the practical application of the VGG-16 and AlexNet models specifically on the ChestX-ray14 dataset. The evaluation of the accuracy of numerous deep-learning models is conducted to assess the efficacy and performance of such models. Among the array of models available, the Genetic Deep Learning Convolutional Neural Network (GDCNN), DenseNet-201, and Convolutional Neural Network (CNN) have emerged as top contenders, showcasing superior performance and robustness. The GDCNN achieved an accuracy of 98.84 percent, and DenseNet-201 exhibited an accuracy of 97.2 percent. Notably, the CNN outperformed the other models with an accuracy of 99.39 percent. The incorporation of a larger dataset, the addition of more convolutional layers to the CNN, and image segmentation techniques may enhance the overall performance and accuracy levels.

Keywords:

Deep Learning Predictive Models,Diseases,Lung Cancer,Pneumonia,Tuberculosis,

Refference:

I. A. H. Alharbi, H.A. Hosni Mahmoud. : ‘Pneumonia Transfer Learning Deep Learning Modelfrom Segmented X-rays’. Healthcare. Vol. 10, (6), pp.987, MDPI.2022, May.
https://doi.org/10.3390/healthcare10060987.
II. A.M. Barhoom, S.S. Abu-Naser. : ‘Diagnosis of Pneumonia Using Deep Learning’. International Journal of Academic Engineering Research. (IJAER). Vol. 6(2) (2022).
https://philpapers.org/rec/BARDOP-3
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IV. A. Munappy, J. Bosch, H. H. Olsson, A. Arpteg, and B. Brinne. : ‘Data management challenges for deep learning’. 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE. Pp. 140-147 (2019). 10.1109/SEAA.2019.00030
V. A. K. Minda and V. Ganesan. : ‘A Review on Optimal Deep Learning Based Prediction Model for Multi Disease Prediction’. Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2022. pp. 81-90 (2023). 10.1007/978-981-19-6880-8_8
VI. A. I. Newaz, A. K. Sikder, M. A. Rahman, and A. S. Uluagac. : ‘A survey on security and privacy issues in modern healthcare systems: Attacks and defenses’. ACM Transactions on Computing for Healthcare. Vol. 2(3), pp. 1-44 (2021). 10.1145/3453176
VII. C. Bhatt, I. Kumar, V. Vijayakumar, K. U. Singh, and A. Kumar. : ‘The state of the art of deep learning models in medical science and their challenges’. Multimedia Systems. Vol. 27(4), pp. 599-613 (2021). 10.1007/s00530-020-00694-1
VIII. C. H. Koh, D. Z. Khan, R. Digpal, H. Layard Horsfall, A. Ali, S. E. Baldeweg, … and H. J. Marcus : ‘The clinical outcomes of imaging modalities for surgical management Cushing’s disease–A systematic review and meta-analysis’. Frontiers in Endocrinology. Vol. 13, 3591, 2023. 10.3389/fendo.2022.1090144
IX. Data source available from : https://nihcc.app.box.com/v/ChestXray-NIHCC
X. E. L. Schwarz, L. Pegolotti, M. R. Pfaller, and A. L. Marsden. : ‘Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease’. Biophysics Reviews. Vol. 4(1), 011301, 2023. 10.1063/5.0109400
XI. F. Jing, Z. Li, S. Qiao, J. Zhang, B. Olatosi, and X. Li. : ‘Using geospatial social media data for infectious disease studies: a systematic review’. International Journal of Digital Earth. Vol. 16(1), pp. 130-157, 2023. 10.1080/17538947.2022.2161652
XII. H. Abbasimehr, and R. Paki. : ‘Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization’. Chaos, SolitonsFractals. Vol. 142, 110511, 2021. 10.1016%2Fj.chaos.2020.110511
XIII. H. Ali, I. K. Niazi, B. K. Russell, C. Crofts, S. Madanian, and D. White. : ‘Review of Time Domain Electronic Medical Record Taxonomies in the Application of Machine Learning’. Electronics. Vol. 12(3), 554, 2023. 10.3390/electronics12030554
XIV. J. Alshudukhi, S. Aljaloud, T.S. Alharbi and S. Abebaw. : ‘Convolutional Neural Network Architecturesto Solve a Problem of Tuberculosis Classification Using X-Ray Images of the Lungs’. Journal of Nanomaterials. 2509830, 2022. https://doi.org/10.1155/2022/2509830
XV. J. He, K. Chen, X. Pan, J. Zhai, and X. Lin. : ‘Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review’. Journal of Semiconductors. Vol. 44(2), 023104, 2023. 10.1088/1674-4926/44/2/023104
XVI. L. Velasquez-Camacho, M. Otero, B. Basile, J. Pijuan and G. Corrado : ‘Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards’. Microorganisms. Vol. 11(1), pp. 73, 2023. 10.3390/microorganisms11010073
XVII. M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad : ‘Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review’. Sensors. Vol. 23(2), pp. 828, 2023. F10.3390/s23020828
XVIII. M. Nahiduzzaman, M.R. Islam, and R. Hassan. : ‘ChestX-Ray6: Prediction of multiple diseasesincluding COVID-19 from chest X-ray images using convolutional neural network’. Expert Systems with Applications, Vol. 211, 118576, 2023. https://doi.org/10.1016/j.eswa.2022.118576
XIX. M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi. : ‘Xnor-net: Imagenet classification using binary convolutional neural networks’. European conference on computer vision. Springer, Cham. pp.525-542 2016. 10.48550/arXiv.1603.05279
XX. N. C. Hurley, E. S. Spatz, H. M. Krumholz, R. Jafari, and B. J. Mortazavi. : ‘A survey of challenges and opportunities in sensing and analytics for risk factors of cardiovascular disorders’. ACM transactions on computing for healthcare. Vol. 2(1), pp. 1-42, 2020. 10.1145/3417958
XXI. N. Kumar, R. K. Kaushal, S. N. Panda, and S. Bhardwaj. : ‘Impact of the Internet of Things and Clinical Decision Support System in Healthcare’. IoT and WSN based Smart Cities: A Machine Learning Perspective. Cham: Springer International Publishing. (pp. 15-26). (2022). 10.1007/978-3-030-84182-9_2
XXII. N. Kumar, M. Gupta, D. Gupta, and S. Tiwari. : ‘Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images’. Journal of ambient intelligence and humanized computing. Vol. 14(1), pp. 469-478, 2023. 10.1007/s12652-021-03306-6
XXIII. N. Sheng, L. Huang, Y. Lu, H. Wang, L. Yang, L. Gao, and Y. Wang, : ‘Data resources and computational methods for lncRNA-disease association prediction’. Computers in Biology and Medicine. 106527, 2023. 10.1016/j.compbiomed.2022.106527
XXIV. P. K. Shukla, J. K. Sandhu, A. Ahirwar, D. Ghai, P. Maheshwary, and P. K. Shukla. : ‘Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images’. Mathematical Problems in Engineering. pp. 1-9, 7804540, 2021. 10.1155/2021/7804540
XXV. P. Szepesi, and L. Szilágyi. : ‘Detection of pneumonia using convolutional neural networks and deeplearning’. Biocybernetics and Biomedical Engineering. Vol. 42(3), pp. 1012-1022, 2022. 10.1016/j.bbe.2022.08.001
XXVI. R. G. Babukarthik, V. A. K. Adiga, G. Sambasivam, D. Chandramohan, and J. Amudhavel. : ‘PredictionofCOVID-19 using genetic deep learning convolutional neural network (GDCNN)’. IEEE Access. Vol. 8, pp. 177647-177666, 2020. 10.1109/access.2020.3025164
XXVII. R. Miotto, F. Wang, S. Wang, X. Jiang and J. T. Dudley. : ‘Deep learning for healthcare: review, opportunities and challenges’. Briefings in bioinformatics. Vol. 19(6), pp. 1236-1246, 2018. 10.1093/bib/bbx044
XXVIII. R. Qureshi, B. Zou, T. Alam, J. Wu, V. Lee and H. Yan, : ‘Computational methods for the analysis and prediction of egfr-mutated lung cancer drug resistance: Recent advances in drug design, challenges and future prospects’. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022). 10.1109/TCBB.2022.3141697
XXIX. S.Y. Lu, S.H. Wang, X. Zhang, and Y.D. Zhang. : ‘TBNet: a context-aware graph network for tuberculosis diagnosis’. Computer Methods and Programs in Biomedicine. Vol. 214, 106587, 2022. 10.1016/j.cmpb.2021.106587
XXX. S. Sengan, L. Arokia Jesu Prabhu, V. Ramachandran, V. Priya, L. Ravi, and V. Subramaniyaswamy. : ‘Images super-resolution by optimal deep AlexNet architecture for medical application: a novel DOCALN’. Journal of Intelligent & Fuzzy Systems. Vol. 39(6), pp. 8259-8272, 2020. 10.3233/JIFS-189146
XXXI. S. H. Karaddi, and L. D. Sharma. : ‘Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks’. Expert Systems with Applications. Vol. 211, 118650, 2023.
XXXII. S. Tammina. : ‘Transfer learning using vgg-16 with deep convolutional neural network for classifying images’. International Journal of Scientific and Research Publications (IJSRP), Vol. 9(10), pp. 143-150, 2019. 10.29322/IJSRP.9.10.2019.p9420
XXXIII. S. Krishnamoorthy, A. Dua, and S. Gupta. : ‘Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: A survey, current challenges and future directions’. Journal of Ambient Intelligence and Humanized Computing. Vol. 14(1), pp. 361-407, 2023. 10.1007/s12652-021-03302-w
XXXIV. S. Shamas, S. N. Panda and I. Sharma. : ‘Review on Lung Nodule Segmentation-Based Lung Cancer Classification Using Machine Learning Approaches’. In Artificial Intelligence on Medical Data: Proceedings of International Symposium, ISCMM 2021. Singapore: Springer Nature Singapore. Pp. 277-286, 2022. 10.1007/978-981-19-0151-5_24
XXXV. S. T. H. Kieu, A. Bade, M.H.A. Hijazi, M. H. A and H. Kolivand. : ‘A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions’. Journal of imaging. Vol. 6(12), 131, 2020. 10.3390/jimaging6120131
XXXVI. S.M. Fati, E.M. Senan, and N. ElHakim. : ‘Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion’. Applied Sciences. Vol. 12(14), 7092, 2022. 10.3390/app12147092
XXXVII. S. Tiwari, S. Kumar, and K. Guleria, K., : ‘Outbreak trends of coronavirus disease–2019 in India: a prediction’. Disaster medicine and public health preparedness. Vol. 14(5), pp. e33-e38, 2020. 10.1017/dmp.2020.115
XXXVIII. V. Kumar, H. Patel, K. Paul, and S. Azad. : ‘Deep Learning Assisted Retinopathy of Prematurity (ROP) Screening’. ACM Transactions on Computing for Healthcare. Vol. 4(3), Article No.: 18, pp. 1–32, 2023. 10.1145/3596223\
XXXIX. V. Sharma, N. Mishra, V. Kukreja, A. Alkhayyat and A. A. Elngar. : ‘Framework for Evaluating Ethics in AI’. 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), Uttarakhand, India. pp. 307-312, 2023. 10.1109/ICIDCA56705.2023.10099747.
XL. W. Wang, M. Zhang, G. Chen, H.V. Jagadish,B.C. Ooi and K. L. Tan. : ‘Database meets deep learning: Challenges and opportunities’. ACM SIGMOD Record. Vol. 45(2), pp. 17-22, 2016. 10.1145/3003665.3003669

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Abstract:

Cloud computing is a prominent technology that allows clients to access the required data to accomplish their tasks on any machine with an internet connection. Although it is an emerging technique in the information technology world, it is facing some challenges also. Data security has become a big hindrance in the growth and promotion of cloud services. As data resides in different places all over the world, data security and privacy have become major areas of concern about cloud technology. Mathematical modelling acts as an important aid to examine and alleviate possible attacks or hazards on cloud models. The paper reviews several security areas and issues related to the cloud computing environment. It also aims to focus mathematical models on security issues that arise from the use of cloud services. Various threats to the data security for a faithful cloud environment are also discussed. Various methods that ensure cloud privacy and security of the data are also reviewed.

Keywords:

Cloud Computing,Cloud environment,data security,Confidentiality,integrity,Mathematical modelling,Threats in a cloud environment,

Refference:

I. A. Seccombe, A. Hutton, A. Meisel, A. Windel, A. Mohammed, & A. Licciardi. : ‘Security guidance for critical areas of focus in cloud computing’. Cloud Security Alliance. Vol. 2(1), 2009.
II. A. Behl. : ‘Emerging security challenges in cloud computing: an insight to cloud security challenges and their mitigation’. Proceedings of the World Congress on Information and Communication Technologies (WICT ’11), IEEE. pp. 217–222, December 2011. 10.1109/WICT.2011.6141247
III. AL-Museelem Waleed, Li Chunlin. : ‘User Privacy and Security in Cloud Computing’. International Journal of Security and Its Applications. Vol. 10(2), pp.341-352, 2016.
IV. A NGENZI et al., : ‘Applying mathematical models in cloud computing: A survey’. IOSR Journal of Computer Engineering. Vol. 16(5), pp. 36-46, 2014. 10.9790/0661-16523646
V. A. Pandey, R. M. Tugnayat, and A. K. Tiwari. : ‘Data Security Framework for Cloud Computing Networks’. International Journal of Computer Engineering & Technology. vol. 4(1), pp. 178–181, 2013.
VI. D. Chen and H. Zhao. : ‘Data security and privacy protection issues in cloud computing’. Proceeding of the International Conference on Computer Science and Electronics Engineering (ICCSEE ’12), Hangzhou, China. vol. 1, pp. 647–651, March 2012. 10.1109/ICCSEE.2012.193
VII. E. Anwar J. Alzaid. : ‘Cloud Computing: An Overview’. International Journal of Advanced Research in Computer and Communication Engineering. September 2013.
VIII. F. Gilbert. : ‘Proposed EU data protection regulation: the good, the bad, and the unknown’. In: Journal of Internet Law. Vol. 15 (10), pp. 20-34, 2012.
IX. H. Zwingelberg, M. Hansen. : ‘Privacy Protection Goals and Their Implications for eID Systems’. In Camenisch, J. (Ed.): Privacy and Identity Management for Life. Springer, Berlin. pp. 14-31, 2012.
X. J. Schiffman, T. Moyer, H. Vijayakumar, T. Jaeger, and P. McDaniel. : ‘Seeding clouds with trust anchors’. Proceedings of the ACM workshop on Cloud computing security workshop (CCSW ’10), ACM. pp. 43–46, October 2010. 10.1145/1866835.1866843

XI. K. D. Bowers, A. Juels, and A. Oprea. : ‘HAIL: a high-availability and integrity layer for cloud storage’. Proceedings of the 16th ACM conference on Computer and Communications Security, ACM, Chicago, Ill, USA, pp. 187–198, November 2009. https://eprint.iacr.org/2008/489
XII. K. D. Bowers, A. Juels, and A. Oprea. : ‘Proofs of retrievability: theory and implementation’. Proceedings of the ACM Workshop on Cloud Computing Security (CCSW’09), pp. 43–53,November 2009. 10.1145/1655008.1655015
XIII. L. Tawalbeh, N.S. Darwazeh, R.S. Al-Qassas and F. AlDosari. : ‘A secure cloud computing model based on data classification’. Elsevier. Vo. 52, pp 1153-1158, 2015. 10.1016/j.procs.2015.05.150
XIV. M. A. Shah, R. Swaminathan, and M. Baker. : ‘Privacy-preserving audit and extraction of digital contents’. IACR Cryptology EPrint Archive, vol. 186, 2008. https://eprint.iacr.org/2008/186
XV. M. Z. Meetei. : ‘Mathematical model of security approaches on cloud computing’. International Journal of Cloud Computing. Vol. 6 (3), pp.187 – 210, 2017. 10.1504/IJCC.2017.086710
XVI. P. Mell and T. Grance. : ‘The nistdefinition of cloud computing’. National Institute of Standards and Technology. vol. 53(6), article 50, 2009. https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-145.pdf
XVII. R. Latif, H. Abbas, S. Assar, and Q. Ali. : ‘Cloud computing risk assessment: a systematic literature review’. Future InformationTechnology, Springer, Berlin, Germany. pp. 285–295, 2014. 10.1007/978-3-642-40861-8_42
XVIII. S. Bollavarapu and B. Gupta. : ‘Data Security in Cloud Computing’. International Journal of Advanced Research in Computer Science and Software Engineering. Vol. 4(3), March 2014.
XIX. S. Ali Abbas, A. A. Baqi Maryoosh. : ‘Improving Data Storage Security in Cloud Computing Using Elliptic Curve Cryptography’. Journal of Computer Engineering. Vol. 17(4), Ver. I, pp. 48-53, 2015.
XX. V. Biksham, Dr. D.Vasumathi. : ‘Query based computations on encrypted data through homomorphic encryption in cloud computing security’. International Conference on Electrical, Electronics, and ptimization Techniques (ICEEOT), 2016 .978-1-4673 9939-5/16, 2016.

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Abstract:

In this paper, we are examining He’s polynomial method for solving (1+1)- dimensional and (2+1)-dimensional heat-like equations that arise in various diffusion processes. The absolute error is calculated from the exact solution and numerical solution by taking different iterations of the He’s polynomial. This method is also called the homotopy perturbation method (HPM). The nonlinear terms can be easily handled by the use of He’s polynomials. The proposed scheme finds the solution without any discretization or restrictive assumptions and avoids round-off errors. Some examples are given to show the efficiency and accuracy of the He’s polynomial used to solve Heat-like equations.

Keywords:

Boundary Conditions,Error Analysis,He’s Polynomial,Heat Equations,Nonlinear Terms,Homotopy ,Perturbation Method,

Refference:

I. Ghorbani. : ‘Beyond Adomian polynomials: He polynomials’. Chaos, Solitons & Fractals. vol. 39(3), pp. 1486–1492, 2009. 10.1016/j.chaos.2007.06.034
II. Ghorbani and J. Saberi-Nadjafi. : ‘He’s homotopy perturbation method for calculating adomian polynomials’. International Journal of Nonlinear Sciences and Numerical Simulation. vol. 8(2), pp. 229–232, 2007. 10.1515/IJNSNS.2007.8.2.229
III. J.-H. He. : ‘A coupling method of a homotopy technique and a perturbation technique for nonlinear problems’. International Journal of Non-Linear Mechanics. vol. 35(1), pp. 37–43, 2000. 10.1016/S0020-
7462(98)00085-7
IV. J.-H. He. : ‘An elementary introduction to recently developed asymptotic methods and nanomechanics in textile engineering’. International Journal of Modern Physics B. vol. 22(21), pp. 3487–3578, 2008. 10.1142/S0217979208048668
V. J.-H. He. : ‘Comparison of homotopy perturbation method and homotopy analysis method’. Applied Mathematics and Computation. vol. 156(2),
pp. 527–539, 2004. 10.1016/j.amc.2003.08.008
VI. J.-H. He. : ‘Homotopy perturbation method for bifurcation of nonlinear problems’. International Journal of Nonlinear Sciences and Numerical Simulation. vol. 6(2), pp. 207–208, 2005. 10.1515/IJNSNS.2005.6.2.207
VII. J.-H. He. : ‘Homotopy perturbation method for solving boundary value problems’. Physics Letters A. vol. 350(1-2), pp. 87–88, 2006. 10.1016/j.physleta.2005.10.005
VIII. J.-H. He. : ‘Recent development of the homotopy perturbation method’, Topological Methods in Nonlinear Analysis’. vol. 31(2), pp. 205–209, 2008.
IX. J.-H. He. : ‘Some asymptotic methods for strongly nonlinear equations’. International Journal of Modern Physics B’. vol. 20(10) pp. 1141–1199, 2006. 10.1142/S0217979206033796
X. J.-H. He. : ‘The homotopy perturbation method for nonlinear oscillators with discontinuities’. Applied Mathematics and Computation. vol. 151(1), pp. 287–292, 2004. 10.1016/S0096-3003(03)00341-2

XI. L. Xu. : ‘He’s homotopy perturbation method for a boundary layer equation in unbounded domain’. Computers & Mathematics with Applications. vol. 54(7-8), pp. 1067–1070, 2007.
10.1016/j.camwa.2006.12.052
XII. M. A. Noor and S. T. Mohyud-Din. : ‘Homotopy perturbation method for solving nonlinear higher order boundary value problems’. International Journal of Nonlinear Sciences and Numerical Simulation. vol. 9(4),
pp. 395–408, 2008. 10.1515/IJNSNS.2008.9.4.395
XIII. M. A. Noor and S. T. Mohyud-Din. : ‘Modified variational iteration method for heat and wave-like equations’. Acta Applicandae Mathematicae. vol. 104(3), pp. 257–269, 2008. 10.1007/s10440-008- 9255-x
XIV. M. A. Noor and S. T. Mohyud-Din. : ‘Modified variation alliteration method for heat and wave-like equations’. Acta Applicandae Mathematicae. vol. 104(3), pp. 257–269, 2008. 10.1007/s10440-008- 9255-x
XV. M. Wazwaz and A. Gorguis. : ‘Exact solutions for heat-like and wave- like equations with variable coefficients’. Applied Mathematics and Computation. vol. 149(1), pp. 15–29, 2004. 10.1016/S0096-
3003(02)00946-3
XVI. R. Wilcox. : ‘Closed-form solution of the differential equation (∂^2/∂x∂y+ax(∂/∂x)+by(∂/∂y)+cxy+∂/∂t)P(x,y,t) = 0 by normal-ordering exponential operators’. Journal of Mathematical Physics. vol. 11(4),
pp. 1235–1237, 1970. 10.1063/1.1665252
XVII. S. T. Mohyud-Din, M. A. Noor, and K. I. Noor. : ‘Traveling wave solutions of seventh-order generalized KdV equations using He’s polynomials’. International Journal of Nonlinear Sciences and Numerical Simulation. vol. 10(2), pp. 227–233, 2009.
10.1515/IJNSNS.2009.10.2.227
XVIII. S. T. Mohyud-Din and M. A. Noor. : ‘Homotopy perturbation method for solving fourth-order boundary value problems’. Mathematical Problems in Engineering’. vol. 2007, Article ID 98602, 15 pages, 2007.
10.1155/2007/98602
XIX. S. T. Mohyud-Din, M. A. Noor, and K. I. Noor. : ‘Homotopy perturbation method for unsteady flow of gas through a porous medium’. International Journal of Modern Physics B. In press.
XX. S. T. Mohyud-Din, M. A. Noor, and K. I. Noor. : ‘On the coupling of polynomials with correction functional’. International Journal of Modern Physicss B. In press.

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Abstract:

India is a developing country in terms of both technology and infrastructure. Since India is already pursuing its research in 5G and is already using the 4G network, but the problem that Indian citizens have with the current mobile network infrastructure can’t be properly solved even by the upcoming 5G, so here we need to think about why we are talking about 6G in such an early stage. Not only because of the great network bandwidth and low latency of the features of 6G, or why we say that 6G will be a game changer for Indian mobile network infrastructure because of features like maximum spectral efficiency that’s up to 1000 km/hours and peak data rate that’s up to 1 Tbps, which is twice that of 5G and nearly quadruple that of 4G. In this paper, we discussed what the main advantages are. What are the problems faced by Indians, why these problems are caused, and how 6G can solve them? We also provide the questions that we asked the people (mainly the youth of India). In addition, our paper also suggests a project idea that provides a technological solution to teachers’ and professors’ wasted time at schools or colleges attending this conference

Keywords:

High-Performance Computing (HPC) Nexus,Internet of Things (IOT),MIMO,6^TH Generation,Low Latency,Network Bandwidth,

Refference:

I. Adams R., & Kim S., : ‘Edge Computing in 6G Networks: Challenges and Opportunities’. IEEE Communications Magazine, Vol. 59(7), pp. 82-88, (2021).
II. Brown T., & Lee, J., : ‘Beyond Speed and Connectivity: Envisioning the Future of 6G Networks’. International Journal of Communication Systems. Vol. 36(9), pp. 1345-1360, (2023).
III. Cho H., et al., : ‘Global Research Initiatives in 6G Wireless Technology: A Comprehensive Review’. Wireless Personal Communications. Vol. 128(3), pp. 1901-1915.
IV. Choudhury A., & Sarkar, S., : ‘Evolution of wireless communication technologies: A comprehensive review’. Wireless Networks. Vol. 28(1), pp. 87-110.
V. Datta R., et al., : ‘Enabling Technologies for 6G Wireless Networks: A Comprehensive Survey’. IEEE Communications Surveys & Tutorials. Vol. 25(1), pp. 357-382, (2023).
VI. Forouzan B. A., : ‘Data Communications and Networking’. McGraw-Hill.(2007).
VII. Garcia A., & Wang Y., : ‘The Evolution of Telecommunications: A Comparative Analysis of 5G and Beyond’. Journal of Telecommunications and Information Technology, Vol. 5(2), pp. 127-141, (2021).
VIII. Gupta A., & Sharma P., : ‘Telemedicine Accessibility Challenges in Rural India: A Network Perspective’. Journal of Telemedicine and Telecare. Vol. 28(2), pp. 87-104, (2022).
IX. Gupta A., et al., : ‘Digital Education in Rural India: Challenges and Opportunities’. International Journal of Educational Development. Vol. 78, 102226, (2020).
X. Gupta M., et al., : ‘Hexa-X: Advancing 6G Research Standards Through Global Collaboration’. Journal of Global Information Technology Management. Vol. 26(4), pp. 307-322, (2023).
XI. Gupta P., et al., : ‘Advancements in 6G Technology and Its Implications for Defence Communication Networks’. International Conference on Defence Technology, pp. 345-360, (2022).
XII. Gupta P., et al., : ‘6G as a linchpin for innovation in smart cities and Industry 4.0’. International Journal of Distributed Sensor Networks. Vol. 19(7), pp. 100-115, (2023).
XIII. Gupta S., & Reddy, N., : ‘Digital Divide in India: An Analysis of Rural-Urban Disparities in Internet Access’. International Journal of Information Management. Vol. 50, pp. 151-163, (2020).
XIV. Johnson L., & Chen, Q., : ‘Technological Advancements in 6G: A Comprehensive Overview’. International Journal of Mobile Communications. Vol. 20(3), pp. 256-271, (2022).
XV. Joshi A., & Prakash A., : ‘Strategic importance of telecommunications in India: A review’. Journal of Economic Structures. Vol. 9(1), pp. 1-15, (2020).
XVI. Khan S., & Verma R., : ‘Data Security in e-Health: A Case Study of Challenges in Indian Healthcare Networks’. International Journal of Medical Informatics. Vol. 148, 104355, (2021).
XVII. Kumar A., & Akhtar Z., : ‘6G wireless communication technology: A comprehensive review’. : ‘Transactions on Emerging Telecommunications Technologies’. : Vol. 34(6), e4300. (2023).
XVIII. Kumar R., & Jain V., : Terahertz communication technology: A review’. IEEE Transactions on Terahertz Science and Technology’. Vol. 12(5), pp. 472-487. (2022).
XIX. Kumar R., & Singh, A., : ‘Network Latency Issues in Defence Operations: A Case Study of the Indian Defence Sector’. : International Journal of Military Communications., Vol. 28(4), pp. 567-582.
XX. Kumar S., et al., : ‘Tailored solutions of 6G technology for the Indian context’. International Journal of Electrical, Computer, and Systems Engineering, Vol. 12(4), pp. 300-315. (2023).
XXI. Kung S.Y., Mak, M.W., Lin, S.H., Mak, M.W. and Lin, S., : ‘Biometric authentication: a machine learning approach’. New York: Prentice Hall Professional Technical Reference. (pp. 27-49).
XXII. Kurose J. F., & Ross, K. W., : ‘Computer Networking: A Top-Down Approach’.Pearson. (2017).
XXIII. Liu X., et al., : ‘Key Players in 6G Development: A Comparative Study’. International Journal of Advanced Networking and Applications’. Vol. 15(4), pp. 212-226, (2023).
XXIV. Mahmood Z., et al., : ‘6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies’. IEEE Open Journal of the Communications Society, Vol. 3, pp. 585-596, (2022).
XXV. Malgheet J. R., Manshor, N. B., Affendey, L. S. and Abdul Halin A. B., : ‘Iris recognition development techniques: a comprehensive review’. Complexity. pp.1-32, 2021.
XXVI. Maltoni D., Maio D., Jain A. K. and Prabhakar S., : ‘Handbook of fingerprint recognition’. London: springer. Vol. 2, 2009.
XXVII. Mishra S., et al., : ‘Paradigm shift in wireless communication: The case of 6G technology’. Journal of Communications. Vol. 18(1), pp. 1-15, (2023).
XXVIII. National Research Council and Whither Biometrics Committee, 2010. Biometric recognition: Challenges and opportunities.
XXIX. Park S., & Singh R., : ‘High Performance Computing in 6G Networks: Challenges and Opportunities’. IEEE Network. Vol. 34(6), pp. 110-117, (2020).
XXX. Rappaport T. S., et al., : ‘Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond’. IEEE Access. Vol. 9, pp. 55059-55078, (2021).
XXXI. Reddy N., et al., : ‘Challenges in Remote Patient Monitoring: A Network- Centric Approach’. Health Information Science and Systems. Vol. 8(1), pp. 1- 14, (2020).
XXXII. Sharma A., & Singh M., : ‘Redefining interactions with 6G technology: Opportunities and challenges’. International Journal of Scientific Research in Computer Science, Engineering and Information Technology’. Vol. 7(3), pp. 1-8, (2022).
XXXIII. Sharma A., et al., : ‘Network Optimization for Congestion Management in Indian Telecom Networks’. Journal of Network and Systems Management. Vol. 27(4), pp. 863-880, (2019).
XXXIV. Sharma R., et al., : ‘The sixth generation wireless networks: Vision, requirements, and enabling technologies’. IEEE Access. Vol. 11, pp. 88101- 88126, (2023).
XXXV. Sharma S., & Singh R., : ‘Digital Learning Ecosystem: Challenges and Opportunities in Indian Higher Education’. Journal of Educational Technology Systems. Vol. 48(4), pp. 446-464, (2019).
XXXVI. Sharma S., et al., : ‘Versatility of 6G technology for addressing societal needs: A case study of India’. International Journal of Scientific Research and Management. Vol. 8(5), pp. 1-10, (2020).
XXXVII. Sharma S., et al., : ‘Interoperability Challenges in Multi-Domain Defence Communications’. Journal of Defence Technology Integration. Vol. 42(1), pp. 89-104, (2023).
XXXVIII. Singh N., et al., : ‘6G: A Revolution in Wireless Communication’. International Journal of Wireless Information Networks, Vol. 30(2), PP. 129-145, (2023).
XXXIX. Singh R., & Kapoor S., : ‘Cybersecurity in India: Challenges and Solutions’. Journal of Cybersecurity’. Vol. 3(1), pp. 45-60, (2018).
XL. Singh V., & Verma P. K., : ‘6G technology: A catalyst for innovation in India’. International Journal of Computer Applications. Vol. 183(5), pp. 20-25, (2022).
XLI. Smith J., & Johnson A., : ‘Academic Contributions to 6G Development: A Review of Recent Advances’. Journal of Telecommunications and Information Technology’. Vol. 4(1), pp. 39-54, (2020).
XLII. Smith J., et al., : ‘Securing Defence Communication Networks: Challenges and Solutions’. Journal of Cybersecurity and Defence Systems., Vol. 15(2), pp. 123- 140, (2022).
XLIII. Srivastava P., & Singh S. K., : ‘A review on the evolution of wireless communication generations: 1G to 5G’. Journal of Advanced Research in Dynamical and Control Systems. Vol. 13(1), pp. 1171-1182, (2021).
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XLVI. Wang X., & Li H., : ‘Country-Specific Involvement in 6G Research and Development: A Comparative Analysis’. International Journal of Wireless Information Networks. Vol. 28(4), pp. 287-302, (2021).
XLVII. Wu Y., et al., : ‘Internet of Things (IoT) Support in 6G Networks: A Review’. International Journal of Communication Systems. Vol. 36(5), pp. 743-758, (2023).
XLVIII. Yadav S., & Agarwal A., : ‘Beyond the buzzwords: Exploring the potential of 6G connectivity’. International Journal of Advanced Networking and Applications. Vol. 13(5), pp. 4051-4060, (2021).

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