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Paper Title | : | Design of Enhanced Skin-Implantable Patch Antenna for Wireless Biomedical Applications |
Authors | : | M. Harish, M. Keerthana, M. Dhamini, K. Rama Krishna, N. Subrahmanyam, G.Shankara Bhaskara Rao |
Affiliations | : | Electronics and Communication Engineering Department Sri Vasavi Engineering College Andhra Pradesh, Tadepalligudem-534101, India. |
Abstract | : | In this paper presents a miniaturized patch antenna designed for skin implantation in the industrial, scientific, and medical (ISM) band (2.40-2.50 GHz). The finite element method using HFSS simulation software was used for simulation purposes. In a homogeneous skin phantom, the proposed antenna achieved a reflection coefficient (S11) of -70.021 dB and a corresponding peak gain of -19.16 dBi at the resonating frequency of 2.418 GHz. The antenna demonstrated a frequency band of 501 MHz (2.252-2.753 GHz) and a percentage bandwidth of 20.1%. Additionally, the calculated maximum specific absorption rate (SAR) met the safety standards outlined by IEEE C95.1-1999 and C95.1-2005. Compared to other designed antennas, the proposed antenna exhibited lower SAR values, higher gain, and improved scattering parameters (S11). To ensure the safety of human tissue, the allowable input power was also calculated. These results indicate that the proposed antenna is suitable for implantable applications. |
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DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i03/v10i0303 |
[1] Piyush kumar Mishra,Saurabh raj,V.S.Tripathi, “A Novel Skin-Implantable Patch Antenna for Biomedical Application,” in IEEE Antennas and Propagation Magazine, https://ieeexplore.ieee.org/document/9376443, 16 March 2021. [2] S. A. A. Shah and H. Yoo, "Scalp-Implantable Antenna Systems for Intracranial Pressure Monitoring," in IEEE Transactions on Antennas and Propagation, vol. 66, no. 4, pp. 2170-2173, April 2018. [3] C. Liu, Y. Guo and S. Xiao, "Capacitively Loaded Circularly Polarized Implantable Patch Antenna for ISM Band Biomedical Applications," in IEEE Transactions on Antennas and Propagation, vol. 62, no. 5, pp. 2407-2417, May 2014. [4] Y. Cho and H. Yoo, "Miniaturised dual-band implantable antenna for wireless biotelemetry," in Electronics Letters, vol. 52, no. 12, pp. 1005-1007, June 2016. [5]https://www.sciencedirect.com/topics/medicine-and-dentistry/medical-telemetry. [6]https://www.rogerscorp.com/advanced-electronics-solutions/rt-duroid-laminates/rt-duroid-6006-and-6010-2lm-laminates. [7] https://www.ansys.com/en-in/products/electronics/ansys-hfss. [8] A. Kiourti, K. A. Psathas, and K. S. Nikita, “Implantable and ingestible medical devices with wireless telemetry functionalities: A review of current status and challenges,” Bio Electro Magn., vol. 35, no. 1, pp. 1–15, Jan. 2014. [9] D. Guha, S. Biswas, and Y. M. M. Antar, “Defected Ground Structure for Microstrip An-tennas, in Microstrip and Printed Antennas: New Trends, Techniques and Applications”, John Wiley & Sons, London, UK, 2011. [10] IEEE Standard for Safety Levels With Respect to Human Exposure to Radio Frequency Electromagnetic Fields, 3kHzto300GHz,IEEEStandard C95.1-2005, 2005. [11]https://www.researchgate.net/post/how_to_reduce_the_resonant_frequency_of_slot_antenna_how_the_slot_shape_should_be_changed_to_reduce_resonance_frequency#:~:text=resonant%20frequency%20can%20be%20reduced,may%20help. [12] S.Gabriel, R.W.Lau, and C.Gabriel,“The dielectric properties of biologicaltissue”,Phys.Med.Biol.pp.2231-2293,Oct.2004. [13] https://www.news-medical.net/health/Insight-into-Implantable-Medical-Devices.aspx
Paper Title | : | Ensemble based Model for Diabetes Prediction and COVID-19 Mortality Risk Assessment in Diabetic Patients |
Authors | : | Dr. Sushma Jaiswal, Ms. Priyanka Gupta, , , |
Affiliations | : | Guru ghasidas University Koni, Bilaspur (C.G.) |
Abstract | : | Millions of individuals worldwide suffer from the chronic ailment known as diabetes, which can harm several organs including the kidneys, eyes, and heart arteries. Diabetes patients are those who are most impacted by COVID 19. This study explores the use of machine learning techniques for predicting diabetes and assessing COVID-19 mortality risks for diabetic patients. The study was conducted on a diabetes and covid 19 datasets containing demographic and clinical features of patients. On both the datasets, the machine learning techniques Support Vector Machine, Random Forest, Multilayer perceptron, naïve bayes and Logistic Regression are used. additionally, their combination is employed to improve the performances of the models. The accuracy, precision, recall, and F1-score of these algorithms were evaluated to determine their performance in predicting diabetes and covid 19. By making this enhancement, healthcare services could use less time, labour, and resources while also making decisions with more reliability. The results of the study showed that the ensemble model had the highest accuracy in predicting diabetes and covid 19. The study also found that older age, higher HbA1c levels, and the presence of comorbidities were significant predictors of mortality risk in diabetic patients with COVID-19. The study concludes that machine learning techniques can be useful in predicting diabetes and assessing COVID-19 mortality risks in diabetic patients, and these findings could aid in developing effective preventive and treatment strategies for diabetes and COVID-19. |
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DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i03/v10i0302 |
[1] T. Beghriche, M. Djerioui, Y. Brik, B. Attallah, and S. B. Belhaouari, “An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/6053824. [2] U. M. Butt, S. Letchmunan, M. Ali, F. H. Hassan, A. Baqir, and H. H. R. Sherazi, “Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications,” J Healthc Eng, vol. 2021, 2021, doi: 10.1155/2021/9930985. [3] M. M. Bukhari, B. F. Alkhamees, S. Hussain, A. Gumaei, A. Assiri, and S. S. Ullah, “An Improved Artificial Neural Network Model for Effective Diabetes Prediction,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/5525271. [4] P. Rajendra and S. Latifi, “Prediction of diabetes using logistic regression and ensemble techniques,” Computer Methods and Programs in Biomedicine Update, vol. 1, p. 100032, 2021, doi: 10.1016/j.cmpbup.2021.100032. [5] L. Roncon, M. Zuin, G. Rigatelli, and G. Zuliani, “Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome,” J Clin Virol, vol. 127, Jun. 2020, doi: 10.1016/J.JCV.2020.104354. [6] H. Khadem, H. Nemat, M. R. Eissa, J. Elliott, and M. Benaissa, “COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework,” Comput Biol Med, vol. 144, May 2022, doi: 10.1016/J.COMPBIOMED.2022.105361. [7] G. Li, Q. Deng, J. Feng, F. Li, N. Xiong, and Q. He, “Clinical Characteristics of Diabetic Patients with COVID-19,” J Diabetes Res, vol. 2020, 2020, doi: 10.1155/2020/1652403. [8] C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” The Lancet, vol. 395, no. 10223, pp. 497–506, Feb. 2020, doi: 10.1016/S0140-6736(20)30183-5. [9] J. jin Zhang et al., “Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China,” Allergy, vol. 75, no. 7, pp. 1730–1741, Jul. 2020, doi: 10.1111/ALL.14238. [10] J. Wu et al., “Influence of diabetes mellitus on the severity and fatality of SARS-CoV-2 (COVID-19) infection,” Diabetes Obes Metab, vol. 22, no. 10, pp. 1907–1914, Oct. 2020, doi: 10.1111/DOM.14105. [11] X. W. Xu et al., “Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series,” BMJ, vol. 368, Feb. 2020, doi: 10.1136/BMJ.M606. [12] G. P. Fadini, M. L. Morieri, E. Longato, and A. Avogaro, “Prevalence and impact of diabetes among people infected with SARS-CoV-2,” J Endocrinol Invest, vol. 43, no. 6, pp. 867–869, Jun. 2020, doi: 10.1007/S40618-020-01236-2. [13] S. Richardson et al., “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area,” JAMA, vol. 323, no. 20, pp. 2052–2059, May 2020, doi: 10.1001/JAMA.2020.6775. [14] M. R. Mehra, S. S. Desai, S. Kuy, T. D. Henry, and A. N. Patel, “Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19,” N Engl J Med, vol. 382, no. 25, p. e102, Jun. 2020, doi: 10.1056/NEJMOA2007621. [15] I. Huang, M. A. Lim, and R. Pranata, “Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia – A systematic review, meta-analysis, and meta-regression,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 4, pp. 395–403, Jul. 2020, doi: 10.1016/J.DSX.2020.04.018. [16] L. Zhu et al., “Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes,” Cell Metab, vol. 31, no. 6, pp. 1068-1077.e3, Jun. 2020, doi: 10.1016/J.CMET.2020.04.021. [17] G. P. Fadini et al., “Newly-diagnosed diabetes and admission hyperglycemia predict COVID-19 severity by aggravating respiratory deterioration,” Diabetes Res Clin Pract, vol. 168, Oct. 2020, doi: 10.1016/J.DIABRES.2020.108374. [18] Y. Mahamat-Saleh et al., “Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies,” BMJ Open, vol. 11, no. 10, Oct. 2021, doi: 10.1136/BMJOPEN-2021-052777. [19] S. Jaiswal and P. Gupta, “MLP-DTP: Performance Evaluation of Diabetes Class Prediction,” IEMECON 2021 - 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks, 2021, doi: 10.1109/IEMECON53809.2021.9689183. [20] H. T. Abbas et al., “Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test,” PLoS One, vol. 14, no. 12, Dec. 2019, doi: 10.1371/JOURNAL.PONE.0219636. [21] K. Vijiyakumar, B. Lavanya, I. Nirmala, and S. Sofia Caroline, “Random Forest Algorithm for the Prediction of Diabetes,” undefined, Mar. 2019, doi: 10.1109/ICSCAN.2019.8878802. [22] M. Alehegn, R. Joshi, and P. Mulay, “Diabetes Analysis And Prediction Using Random Forest, KNN, Naïve Bayes, And J48: An Ensemble Approach,” undefined, 2019. [23] H. Salem, M. Y. Shams, O. M. Elzeki, M. A. Elfattah, J. F. Al?amri, and S. Elnazer, “Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes,” Applied Sciences 2022, Vol. 12, Page 950, vol. 12, no. 3, p. 950, Jan. 2022, doi: 10.3390/APP12030950. [24] S. Islam Ayon and Md. Milon Islam, “Diabetes Prediction: A Deep Learning Approach,” International Journal of Information Engineering and Electronic Business, vol. 11, no. 2, pp. 21–27, Mar. 2019, doi: 10.5815/IJIEEB.2019.02.03. [25] A. K. Dwivedi, “Analysis of computational intelligence techniques for diabetes mellitus prediction,” Neural Comput Appl, vol. 30, no. 12, pp. 3837–3845, Dec. 2018, doi: 10.1007/S00521-017-2969-9/FIGURES/7.
Paper Title | : | Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features |
Authors | : | Dr. Sushma Jaiswal, Mr. Manoj Kumar Pandey, , , |
Affiliations | : | CSIT Department, Guru Ghasidas Central University, Bilaspur, CG |
Abstract | : | A blind color image watermarking using deep artificial neural network (DANN) in YCbCr color model has been proposed aiming at achieving fair trade-off between imperceptibility and robustness. In the proposed watermarking a random generated watermark of length 512 bit is used for the training purpose and original watermark of length 512 bits is used for the testing. Principal component analysis (PCA) is applied to select the best 10 features out of 18 statistical features. Binary classification is used for watermark extraction. It shows the average imperceptibility of 33.34 dB and average SSIM of 0.9860 for four images Lena, Peppers, Mandril and Jet. It performs well in terms of balancing the imperceptibility and robustness, for the threshold value 32. The proposed scheme takes 7.56 seconds for watermark embedding and extraction. It also shows good robustness against common image attacks including the combination of image attacks except the gaussian noise with intensity 0.06 and cropping 20% attacks. The experiment result shows that the proposed watermarking technique performs well against other technique. |
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DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i03/v10i0301 |
[1] Hadipour, A., & Afifi, R. (2020). Advantages and disadvantages of using cryptography in Steganography. 17th International ISC conference on information security and cryptology (ISCISC), 88-94. [2] Biermann, & Christopher J. (1996). Handbook of Pulping and Papermaking (2 edition). San Diego, California, USA, Academic Press, ISBN 0-12-097362-6. [3] Kumar, S., Singh B. K., & Yadav, M. (2020). A Recent Survey on Multimedia and Database Watermarking. Multimedia Tools and Applications (2020) 79:20149–20197. [4] Anand, A., & Singh, A. K. (2020). An improved DWT-SVD domain watermarking for medical information security. Computer Communication. 152, 72-80. https://doi.org/10.1016/j.comcom.2020.01.038. [5] Ernawan, F., & Kabir, M. N. (2020). A block-based RDWT-SVD image watermarking method using human visual system characteristics. The visual computer, 36(1), 19-37. [6] Thanki, R., Kothari, A. & Borra, S. (2021). Hybrid, blind and robust image watermarking: RDWT–NSCT based secure approach for telemedicine applications. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-021-11064-y [7] Cedillo-Hernandez, M., Cedillo-Hernandez, A., & Garcia-Ugalde, F. J. (2021). Improving dft-based image watermarking using particle swarm optimization algorithm. Mathematics, 9(15), 1795. https://doi.org/10.3390/math9151795 [8] Liu, S. Pan, Z. & Song, H. (2017). Digital Image Watermarking Method Based on DCT and Fractal Encoding. IET Image Process. 11, 815–821. [9] Moosazadeh, M. & Ekbatanifard, G. (2019). A new DCT-based robust image watermarking method using teaching-learning-based optimization. Journal of Information Security and Applications. 47, 28-38. https://doi.org/10.1016/j.jisa.2019.04.001 [10] Verma, V.S., Jha, R.K., & Ojha, A. (2015). Digital watermark extraction using support vector machine with principal component analysis based feature reduction. J Vis Communication Image Represent, 31, 75–85. [11] Islam, M., Roy, A. & Laskar, R.H. (2020). SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Computing & Application, 32, 1379–1403. [12] Zear, A., Singh, P. K., (2021). Secure and robust color image dual watermarking based on LWT-DCT-SVD, multimedia tools and applications. https://doi.org/10.1007/s11042-020-10472-w [13] Mellimi, S., Rajput, V., Ansari, I.A., & Ahn, C.W. (2021). A fast and efficient image watermarking scheme based on Deep Neural Network. Pattern Recognition Letters, 151, 222-228. [14] Pandey, M.K., Parmar, G., Gupta, R., Sikander, A. (2018). Non-blind Arnold scrambled hybrid image watermarking in YCbCr color space. Microsystem Technologies. https://doi.org/10.1007/s00542-018-4162-1 [15] Patvardhan, C., Kumar, P., Lakshmi, C.V. (2017). Effective color image watermarking scheme using YCbCrcolor space and QR code. Multimed Tools Appl. DOI 10.1007/s11042-017-4909-1 [16] Chang, T. J., Pan, I. H., Huang, P. S., & Hu, C. H. (2018). A robust DCT-2DLDA watermark for color images. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-6505-4. [17] Mahto, D.K., Anand, A. & Singh, A.K. (2022). Hybrid optimisation-based robust watermarking using denoising convolutional neural network. Soft Computing. https://doi.org/10.1007/s00500-022-07155-z [18] Abdelhakim, A. M., Abdelhakim, M. (2018). A time-efficient optimization for robust image watermarking using machine learning. Expert Syst Appl 100:197–210. [19] Abdulrahman, A. K., Ozturk S (2019) A novel hybrid DCT and DWT based robust watermarking algorithm for color images. Multimed Tools Appl 78(12):17027–17049 [20] Kang, X., Chen, Y., Zhao, F., Lin, G. (2020). Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain. Soft Computing 24, 10561–10584 (2020). https://doi.org/10.1007/s00500-019-04563-6. [21] Sharma, S., Sharma, H., Sharma, J. B. (2021) Artifcial bee colony based perceptually tuned blind color image watermarking in hybrid lwtdct domain. Multimed Tools Appl 80(12):18753–1878. [22] Jaiswal, S. and Pandey, M. K., (2022). Robust digital image watermarking using LWT and Random-Subspace-1DLDA with PCA based statistical feature reduction. 2022 Second International Conference on Computer Science, Engineering and Applications, IEEE, 1-6. [23] Sharma, S., Sharma, H., Sharma, J. B., Poonia, R. C. (2020). A secure and robust color image watermarking using nature inspired intelligence. Neural Computing and Application. https://doi.org/10.1007/s00521-020-05634-8
Paper Title | : | Future of Communication-LIFI (Light Fidelity): A Review |
Authors | : | Mr. Hilal Ahmad Shah, Mr. Inzimam Ul Hassan, Mr. Inam ul Haq, , |
Affiliations | : | Chandigarh University |
Abstract | : | As on date internet has made the revolution in the world. Whether you are using internet in a coffee shop, offices or at home. Speed of the internet is major issue. with the advent of technology, communication became the backbone of ICT. ICT had made our globe like a town. Today everyone (Business, institutions, organizations, entrepreneurs is thrust for getting right information at the right time and right place. Which, requires fast internet connectivity, Technology and large spectrum of channels. Present paper reflects the Future of Communication (LI-FI) which may affect all lives. It a technology that may be as fast as 500MBPS (30GBPS per minute) an alternative, cost effective and more robust and useful than Wi-Fi. The Visible light communication which may be the future of Internet |
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DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i02/v10i0202 |
[1] R. George, S. Vaidyanathan, A. S. Rajput, and K. Deepa, “LiFi for Vehicle to Vehicle Communication - A Review,” Procedia Comput. Sci., vol. 165, no. 2019, pp. 25–31, 2019, doi: 10.1016/j.procs.2020.01.066. [2] D. Sharma and R. Malhotra, “Infrared ( IR ) Serial Communication,” vol. 10, no. 5, pp. 99–102, 2021, doi: 10.17148/IJARCCE.2021.10518. [3] H. Haas, C. Chen, and D. O’Brien, “A guide to wireless networking by light,” Prog. Quantum Electron., vol. 55, no. June, pp. 88–111, 2017, doi: 10.1016/j.pquantelec.2017.06.003. [4] M. de Oliveira, L. C. Vieira, F. P. Guiomar, L. N. Alves, P. P. Monteiro, and A. A. P. Pohl, “Experimental assessment of the performance of cooperative links in visible light communications,” Opt. Commun., vol. 524, no. July, p. 128771, 2022, doi: 10.1016/j.optcom.2022.128771. [5] Z. Xu, W. Liu, Z. Wang, and L. Hanzo, “Petahertz communication: Harmonizing optical spectra for wireless communications,” Digit. Commun. Networks, vol. 7, no. 4, pp. 605–614, 2021, doi: 10.1016/j.dcan.2021.08.001. [6] A. Saha, S. Chatterjee, and A. Kundu, “Analysis on Data Transmission using LIFI,” 2020 IEEE Int. Conf. Converg. Eng. ICCE 2020 - Proc., pp. 352–356, 2020, doi: 10.1109/ICCE50343.2020.9290591. [7] M. Leba, S. Riurean, and A. Lonica, “LiFi - The path to a new way of communication,” Iber. Conf. Inf. Syst. Technol. Cist., no. Vlc, 2017, doi: 10.23919/CISTI.2017.7975997. [8] M. Usama, M. Usama, K. Saeed, and A. Yousaf, “A Review on Nomadic Access of Li-Fi Technology A Review on Nomadic Access of Li-Fi Technology,” no. March, pp. 0–4, 2016. [9] G. Kant, V. Gogate, and V. Kotak, “Li-Fi Need of 21 st Century,” no. 2, 2017. [10] H. Haas, “LiFi is a paradigm-shifting 5G technology,” Rev. Phys., vol. 3, no. October 2017, pp. 26–31, 2018, doi: 10.1016/j.revip.2017.10.001. [11] C. Jenila and R. K. Jeyachitra, “Green indoor optical wireless communication systems: Pathway towards pervasive deployment,” Digit. Commun. Networks, vol. 7, no. 3, pp. 410–444, 2021, doi: 10.1016/j.dcan.2020.09.004. [12] Suparyanto dan Rosad (2015, “??No Title No Title No Title,” Suparyanto dan Rosad (2015, vol. 5, no. 3, pp. 248–253, 2020.
Paper Title | : | A Review on the Innovation of Renewable Energy System |
Authors | : | Ms. Avril Anne S. Bernal, Mr. Joemark D. Ablian, , , |
Affiliations | : | Polytechnic College of Botolan |
Abstract | : | Background/Objectives: The world is at a crucial crossroads, with the choice between continued reliance on non-renewable energy sources and embracing a cleaner, greener future powered by renewable energy. Renewable energy sources such as solar and wind power have been prioritized as the best options for future investment due to their unlimited potential. The research highlights the importance of transitioning towards renewable energy and the need for a comprehensive approach to integrate these sources into energy systems. Methods: This literature review examines and analyse existing research on the innovation of renewable energy system. Findings: This transition is crucial for a sustainable future and a sustainable power system. It has an opportunity to emerge as a leader in sustainable energy solutions by optimizing the integration of solar and wind energy through a cutting-edge scheduling model, dedicating a ministry to renewable energy, phasing out harmful subsidies, and mandating solar energy in housing. To make this transition, it should take a proactive approach, including incentives for private investment in renewable energy projects, promoting research and innovation, developing a comprehensive grid system, partnering with the private sector, and providing training and education programs. The study emphasizes the importance of immediate action and a concerted effort to ensure a sustainable future. The impact of policies and initiatives aimed at optimizing renewable energy integration, phasing out subsidies, and improving building efficiency can be studied to inform future efforts towards a more sustainable energy future. In conclusion, the transition towards renewable energy sources is a critical step towards a sustainable future and plays a significant role in achieving this through a holistic approach. |
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DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i02/v10i0201 |
[1] International Energy Agency. (2020). Renewable Energy: Market and Industry Trends. Retrieved from https://www.iea.org/reports/global-energy-review-2020/renewables [2] International Energy Agency. (2023). Renewable Energy Medium-Term Market Report 2023. Retrieved from https://www.iea.org/reports/renewable-energy-market-update-may-2022 [3] International Renewable Energy Agency. (2021). Renewable Power Generation Costs in 2019. Retrieved from https://www.irena.org/reports/2021/May/Renewable-Power-Generation-Costs-in-2019 [4] National Renewable Energy Laboratory (NREL). (2018). Advances in materials and manufacturing for solar and wind technologies. Retrieved from https://www.nrel.gov/docs/fy18osti/70654.pdf [5] European Commission Joint Research Centre. (2019). Bioenergy: Developing new technologies. Retrieved from https://ec.europa.eu/jrc/en/publication/bioenergy-developing-new-technologies [6] Renewable Energy Policy Network for the 21st Century. (2012). Renewables 2012 Global Status Report. Retrieved from https://www.ren21.net/wp-content/uploads/2019/07/REN12-GSR_2012_Full_Report.pdf [7] Lawrence Berkeley National Laboratory. (2017). Tracking the Sun IX: The Decline in Solar Prices. Retrieved from Lawrence Berkeley National Laboratory. (2017). Tracking thttps://emp.lbl.gov/sites/all/files/lbnl-1004234.pdf [8] Nunez, C. (2019, January 22). CAUSES AND EFFECTS OF CLIMATE CHANGE. Environment. Retrieved from https://www.nationalgeographic.com/environment/article/global-warming-overview [9] Klimenko, V., Ratner, S., & Tereshin, A. (2020, April 9). Constraints imposed by key-material resources on renewable energy development. Renewable and Sustainable Energy Reviews. doi:10.1016/j.rser.2021.111011 [10] Kaygusuz, K. (2012, February). Energy for sustainable development: A case of developing countries. Renewable and Sustainable Energy Reviews, 16(2), 1116-1126. doi:10.1016/j.rser.2011.11.013 [11] Asumadu-Sarkodie, S., & Owusu, P. (2016). Feasibility of biomass heating system in Middle East Technical University, Northern Cyprus campus. . Cogent Engineering , 3 . doi:doi:10.1080/23311916.2015.1134304 [12] Hák, T., Janoušková, S., & Moldan, B. (2016). Sustainable development goals: A need for relevant indicators. Ecological Indicators, 60, 565–573. doi:10.1016/j.ecolind.2015.08.003 [13] Owusu, P., Asumadu-Sarkodie, S., & Ameyo, P. (2016). A review of Ghana’s water resource management and the future prospect. Cogent Engineering. doi:doi:10.1080/23311916.2016.1164275 [14] Alvarez-Herranza, A., Balsalobre-Lorente, D., Shahbaz, M., & Cantos, J. (2017, June). Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy, 105, 386-397. doi:10.1016/j.enpol.2017.03.009 [15] Irandoust, M. (2016). The renewable energy-growth nexus with carbon emissions and technological innovation: Evidence from the Nordic countries. Ecological Indicators, 69, 118-125. doi:10.1016/j.ecolind.2016.03.051 [16] Bai, C., Feng, C., Wang, Y., & Gong, Y. (2020, August). Understanding spatial-temporal evolution of renewable energy technology innovation in China: Evidence from convergence analysis. Energy Policy, 143. doi:10.1016/j.enpol.2020.111570 [17] Kittner, N., Lill, F., & Kammen, D. (2017). Energy storage deployment and innovation for the clean energy transition. Nature Energy. doi::10.1038/nenergy.2017.125 [18] Sadorsky, P. (2020, July 2). Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production. doi:10.1016/j.jclepro.2020.125779 [19] Irfan , M., Zhao, Z., & Ahma, M. (2019, February 25). Solar Energy Development in Pakistan: Barriers and Policy Recommendations. Irfan, Muhammad; Zhao, Zhen-Yu; Ahmad, Munir; Mukeshimana, Marie (2019). Solar Energy DevelopmSustainability. doi:10.3390/su11041206 [20] Patil, M., & Vadirajacharya, K. (2019, January). PERFORMANCE IMPROVEMENT OF RENEWABLE ENERGY SOURCES INVERTER FOR INTERFACE WITH SMART GRID. INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR.ORG), 6(1), 157-163. doi:10.1729/Journal.19309 [21] Rudestam, K., & Newton, R. (1992). Surviving your dissertation: A comprehensive guide to content and process. [22] Fink, & Arlene. (2014). Conducting Research Literature Reviews: From the Internet to Paper. [23] Alizadeh, R., Soltanisehat, L., Lund, P., & Zamanisabzi, H. (2019, May 21). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy. doi:10.1016/j.enpol.2019.111174 [24] Li, Y., Wang, C., Li, G., Wang, J., Zhao, D., & Chen, C. (2019, September 14). Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings. doi:10.1016/j.enconman.2020.112526 [25] Fan, M., Sun, K., Lane, D., & Gu, W. (2019, February). A Novel Generation Rescheduling Algorithm to Improve Power System Reliability With High Renewable Energy Penetration. IEEE Transactions on Power Systems for Publication. doi:10.1109/TPWRS.2018.2810642 [26] Xiao, H., Pei, W., Deng, W., Ma, T., Zhang, S., & Kong, L. (2020, September 1). Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection. Applied Energy. doi:10.1016/j.apenergy.2020.116387 [27] Gungah, A., Emodi, N., & Dioha, M. (2018, April 6). Improving Nigeria's renewable energy policy design: A case study approach. Energy Policy. doi:10.1016/j.enpol.2019.03.059 [28] Khan, A., Chenggang, Y., Hussain, J., & Kui, Z. (2020, Septemper 17). Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in belt & Road Initiative countries. Renewable Energy. doi:10.1016/j.renene.2021.02.075 [29] Guen, M., Mosca, L., Perera, A., Coccolo, S., Mohajeri, N., & Scartezzini, J. (2017, June 27). Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration. Energy and Buildings. doi:10.1016/j.enbuild.2017.10.057 [30] Abdelwahab, H., Moussaid, L., Moutaouakkil, F., & Medromi, H. (2018, July 30). Energy Efficiency: Improving the renewable energy penetration in a smart and green community. Procedia Computer Science. doi:10.1016/j.procs.2018.07.199 [31] Strielkowski , W., Civín, L., Tarkhanova, E., Tvaronavi?ien?, M., & Petrenko, Y. (2021, October 28). Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review. Energies. doi:10.3390/en14248240 [32] Hassanein, W. S., Ahmed, M. M., M, O., & Ashmawy, M. G. (2020). Performance Improvement of Off-Grid Hybrid Renewable Energy System Using Dynamic Voltage Restorer. Alexandria Engineering, 1567-1581. doi:10.1016/j.aej.2020.03.037 [33] Gielen, D., Boshell, F., Saygin, D., Bazilian, M., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews. Retrieved from https://doi.org/10.1016/j.esr.2019.01.006 [34] Usman, R., Mirzania, P., Alnaser, S. , & Hart, P. (2022). Systematic Review of Demand-Side Management Strategies in Power Systems of Developed and Developing Countries. Energies. Energies. Retrieved from http://dx.doi.org/10.3390/en15217858 [35] Uwineza, L., Kim, H., & Kim, C. (2021). Feasibility study of integrating the renewable energy system in Popova Island using the Monte Carlo model and HOMER. Energy Strategy Reviews. Retrieved from https://doi.org/10.1016/j.esr.2020.100607 [36] Weidner, T., Martin, A., Ryberg, M., & Gosalbez, G. (2022). Energy systems modeling and optimization for absolute environmental sustainability: current landscape and opportunities. Computers & Chemical Engineering. Retrieved from https://doi.org/10.1016/j.compchemeng.2022.107883 [37] Sadollah, A., Nasir, M., & Geem, Z. (2020). Sustainability and Optimization: From Conceptual Fundamentals to Applications. Retrieved from https://doi.org/10.3390/su12052027 [38] Kabeyi, M., & Olanrewaju, O. (2022). Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply. Sec. Sustainable Energy Systems. Retrieved from https://doi.org/10.3389/fenrg.2021.743114 [39] Bhattarai, U., Maraseni, T., & Apan, A. (2022). Assay of renewable energy transition: A systematic literature review. Science of The Total Environment. Retrieved from https://doi.org/10.1016/j.scitotenv.2022.155159 [40] Future Policy. (2023). Accelerate the Transition to 100% Renewable Energies. World Future Country. Retrieved from https://www.futurepolicy.org/policy-area/renewable-energies/ [41] Cholewa, Mammadov, & Nowaczek. (2022). The obstacles and challenges of transition towards a renewable and sustainable energy system in Azerbaijan and Poland. Mineral Economic. Retrieved from https://link.springer.com/article/10.1007/s13563-021-00288-x
Paper Title | : | Assessing the Performance of Python Data Visualization Libraries: A Review |
Authors | : | Addepalli Lavanya, Lokhande Gaurav, Sakinam Sindhuja, Hussain Seam, Mookerjee Joydeep, Vamsi Uppalapati, Waqas Ali, Vidya Sagar S.D |
Affiliations | : | Universidad Politécnica De Valencia, Valencia, Spain |
Abstract | : | Python is one of the most widely used programming languages for data analysis, visualization, and machine learning. One of Python's key strengths is its rich library ecosystem that provides powerful data visualization tools. Several Python data visualization libraries have emerged in recent years, making it challenging for data analysts and scientists to choose the right library for their visualization needs. Therefore, this research paper aims to assess the performance of Python data visualization libraries and comprehensively review their strengths and limitations. The research paper begins by providing an overview of the most popular Python data visualization libraries, including Matplotlib, Seaborn, Plotly, Bokeh, Altair, and ggplot. We then evaluate each library's performance in terms of its functionality, ease of use, flexibility, and speed.. Additionally, we assess the visual quality of the plots produced by each library and compare them to industry standards. We evaluate the performance of each library by testing them on various datasets and use cases, including large and small datasets, static and interactive visualizations, and different plot types, such as scatter plots, line plots, bar charts, and heatmaps. Our findings suggest that each library has unique strengths and limitations, making choosing one library that fits all visualization needs difficult. However, Matplotlib, Seaborn, and Plotly are the most popular and widely used Python data visualization libraries, each with unique strengths. Matplotlib is a powerful and flexible library that offers a broad range of plotting options, making it ideal for creating complex and customized plots. Seaborn is a high-level library that simplifies the plotting process by providing a consistent interface and easy-to-use functions. Plotly is an interactive visualization library offering rich features for creating web-based visualizations and dashboards. We also find that Bokeh, Altair, and ggplot are less popular but offer unique features and functionality. Bokeh is a library for creating interactive visualizations and dashboards, while Altair is a declarative visualization library that simplifies the plotting process by enabling users to create plots using a simple and intuitive syntax. ggplot is a library that offers a grammar of graphics approach to plotting, making it ideal for users familiar with the R programming language. Overall, this research paper provides a comprehensive review of the most popular Python data visualization libraries and their performance in terms of functionality, ease of use, flexibility, and speed. The findings of this research can help data analysts and scientists choose the a good library for their visualization needs to be based on their specific requirements. Additionally, this research paper can provide a starting point for future research on improving the performance and functionality of Python data visualization libraries. |
![]() | : | 10.22362/ijcert/2023/v10/i01/v10i0104 |
DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i01/v10i0104 |
[1] S. Cao, Y. Zeng, S. Yang, and S. Cao, "Research on Python data visualization technology," in Journal of Physics: Conference Series, vol. 1757, 2021, p. 012122. [2] I. Stanand A. Jovic', "An overview and comparison of free Python li- braries for data mining and big data analysis," in 2019 42nd International convention on information and communication technology, electronics and microelectronics (MIPRO), 2019, pp. 977–982. [3] K. Dale, Data Visualization with Python and JavaScript. [4] M. C. Mihaescu and P. S. Popescu, "Review on publicly available datasets for educational data mining," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 11, no. 3, p. e1403, 2021. [5] M. L. Waskom, "Seaborn: statistical data visualization," Journal of Open Source Software, vol. 6, no. 60, p. 3021, 2021. [6] I. Stanand A. Jovic', "An overview and comparison of free Python li- braries for data mining and big data analysis," in 2019 42nd International convention on information and communication technology, electronics and microelectronics (MIPRO), 2019, pp. 977–982. [7] T. Zhang and L. Mei, "Analysis and research on computer visualization in data science with bokeh and JavaScript," in Journal of Physics: Conference Series, vol. 2033, 2021, p. 012154. [8] A. Batch and N. Elmqvist, "The interactive visualization gap in initial exploratory data analysis," IEEE transactions on visualization and computer graphics, vol. 24, no. 1, pp. 278–287, 2017. [9] R. Wang, Y. Perez-Riverol, H. Hermjakob, and J. A. Vizcaíno, "Open source libraries and frameworks for biological data visualization: A guide for developers," Proteomics, vol. 15, no. 8, pp. 1356–1374, 2015. [10] X. Lou, S. V. D. Lee, and S. Lloyd, "AIMBAT: A python/matplotlib tool for measuring teleseismic arrival times," Seismological Research Letters, vol. 84, no. 1, pp. 85–93, 2013. [11] R. Kumar, "Future for scientific computing using Python," International Journal of Engineering Technologies and Management Research, vol. 2, no. 1, pp. 30–41, 2015. [12] C. Rossant, Learning IPython for interactive computing and data visualization. Packt Publishing Ltd, 2015. [13] D. Rolon-Mérette, M. Ross, T. Rolon-Mérette, and K. Church, "In- troduction to Anaconda and Python: Installation and setup," Quant. Methods Psychol, vol. 16, no. 5, pp. 3–11, 2016. [14] W. S. Pittard and S. Li, "The essential toolbox of data science: Python, R, Git, and Docker," Computational Methods and Data Analysis for Metabolomics, pp. 265–311, 2020. [15] P. Bruce, A. Bruce, and P. Gedeck, Practical statistics for data scientists: 50+ essential concepts using R and Python. O'Reilly Media, 2020. [16] M. Allen, D. Poggiali, K. Whitaker, T. R. Marshall, and R. A. Kievit, "Raincloud plots: a multi-platform tool for robust data visualization," Wellcome open research, vol. 4, 2019. [17] D. P. Kroese, Z. Botev, T. Taimre, and R. Vaisman, Data science and machine learning: mathematical and statistical methods. CRC Press, 2019. [18] C. Sievert, Interactive web-based data visualization with R, plotly, and shiny. CRC Press, 2020. [19] S. M. Ali, N. Gupta, G. K. Nayak, and R. K. Lenka, "Big data visual- ization: Tools and challenges," in 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016, pp. 656–660. [20] I. Stanand A. Jovic', "An overview and comparison of free Python li- braries for data mining and big data analysis," in 2019 42nd International convention on information and communication technology, electronics and microelectronics (MIPRO), 2019, pp. 977–982. [21] C. Gubala and L. Melonçon, "Data Visualizations: An Integrative Literature Review of Empirical Studies Across Disciplines," in 2022 IEEE International Professional Communication Conference (ProComm), 2022, pp. 112–119. [Online]. Available: 10.1109/ProComm53155.2022.00024 [22] L. Podo and P. Velardi, "Plotly. plus, an Improved Dataset for Visualiza- tion Recommendation," in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 4384–4388. [23] K. Jolly, Hands-on data visualization with Bokeh: Interactive web plotting for Python using Bokeh. Packt Publishing Ltd, 2018. [24] C. Chai, C. J. Ammon, M. Maceira, and R. B. Herrmann, "Interactive visualization of complex seismic data and models using Bokeh," Seis- mological Research Letters, vol. 89, no. 2A, pp. 668–676, 2018. [25] D. O. Embarak and O. Embarak, "Data visualization," Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems, pp. 293–342, 2018. [26] S. A. Fahad and A. E. Yahya, "Big data visualization: Allotting by r and python with gui tools," in 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2018, pp. 1–8. [27] D. Y. Chen, Pandas for everyone: Python data analysis. Addison- Wesley Professional, 2017. [28] P. Lemenkova, "Processing oceanographic data by Python libraries NumPy, SciPy and Pandas," Aquatic Research, vol. 2, no. 2, pp. 73– 91, 2019. [29] A. Pal and P. K. S. Prakash, Practical time series analysis: master time series data processing, visualization, and modeling using Python. Packt Publishing Ltd, 2017. [30] T. Petrou, Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python. Packt Publishing Ltd, 2017. [31] C. R. Harris, K. J. Millman, S. J. V. D. Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, and N. J. Smith, "Array programming with NumPy," Nature, vol. 585, no. 7825, pp. 357–362, 2020. [32] P. Lemenkova, "Processing oceanographic data by Python libraries NumPy, SciPy and Pandas," Aquatic Research, vol. 2, no. 2, pp. 73– 91, 2019. [33] W. McKinney, Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. [34] ——, "Pandas, python data analysis library," URL http://pandas. pydata. org, pp. 3–15, 2015. [35] C. Fuhrer, J. E. Solem, and O. Verdier, Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas. Packt Publishing Ltd, 2021. [36] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, "Scikit-learn: Machine learning in Python," the Journal of machine Learning research, vol. 12, pp. 2825–2830, 2011. [37] O. Kramer and O. Kramer, "Scikit-learn," Machine learning for evolu- tion strategies, pp. 45–53, 2016. [38] J. Hao and T. K. Ho, "Machine learning made easy: a review of scikit- learn package in python programming language," Journal of Educational and Behavioral Statistics, vol. 44, no. 3, pp. 348–361, 2019. [39] R. Garreta and G. Moncecchi, Learning scikit-learn: machine learning in Python. Packt Publishing Ltd, 2013. [40] K. Ravishankara, V. Dhanush, and I. S. Srajan, "Whatsapp Chat Ana- lyzer," International Journal of Engineering Research & Technol- ogy, vol. 9, no. 5, pp. 897–900, 2020. [41] T. Haslwanter, "An Introduction to Statistics with Python," With Ap- plications in the Life Sciences.. Switzerland: Springer International Publishing, 2016. [42] E. Bisong and E. Bisong, "Matplotlib and seaborn," Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 151–165, 2019. [43] I. Stanand A. Jovic', "An overview and comparison of free Python li- braries for data mining and big data analysis," in 2019 42nd International convention on information and communication technology, electronics and microelectronics (MIPRO), 2019, pp. 977–982. [44] D. O. Embarak and O. Embarak, "Data visualization," Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems, pp. 293–342, 2018. [45] E. Dabbas, Interactive Dashboards and Data Apps with Plotly and Dash: Harness the power of a fully fledged frontend web framework in Python- no JavaScript required. Packt Publishing Ltd, 2021. [46] J. VanderPlas, B. Granger, J. Heer, D. Moritz, K. Wongsuphasawat, A. Satyanarayan, E. Lees, I. Timofeev, B. Welsh, and S. Sievert, "Altair: interactive statistical visualizations for Python," Journal of open source software, vol. 3, no. 32, p. 1057, 2018. [47] S. A. Fahad and A. E. Yahya, "Big data visualization: Allotting by r and python with gui tools," in 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2018, pp. 1–8. [48] D. O. Embarak and O. Embarak, "Data visualization," Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems, pp. 293–342, 2018. [49] A. Cuttone, S. Lehmann, and J. E. Larsen, "Geoplotlib: a python toolbox for visualizing geographical data," arXiv preprint arXiv:1608.01933, 2016. [50] C. Room, "Machine Learning in Python," algorithms, vol. 8, no. 46, p. 30, 2022.
Paper Title | : | Design And Simulation For 8-Shape 2x4 Slotted Array Antenna Using Ultra Wideband Applications |
Authors | : | G. Shankara Bhaskara Rao, P.V.V Mahesh Kumar, R.P. Trinadh Babu, P. Saranya, Naveen, S.Bhanu Prakash |
Affiliations | : | 1 Associate Professor 2,3,4,5,6UG Students, Department of Electronics and Communication Engineering .Sri Vasavi Engineering College(Autonomous), Tadepalligudem, Andhra Pradesh, India |
Abstract | : | This paper presents a detailed study of the design, simulation, and characterization of a 2x4 slotted microstrip patch antenna array. The antenna array is fed by a microstrip transmission line and is designed on an FR4 substrate with dimensions of 130mm×150mm×1.60mm. The antenna is optimized to operate in two different frequency bands, S and C, respectively. The design process of the antenna array involved four iterations, which allowed for an optimal antenna design to be achieved. The proposed antenna exhibits excellent performance, as indicated by its S11 parameter, gain, and VSWR. The S11 parameter, which is a measure of how much power is reflected back from the antenna, is -22.77dB, indicating low levels of reflection and good impedance matching. The gain of the proposed antenna is 8.19dB, which represents the amount of power that is radiated in a specific direction relative to an isotropic radiator. This is a relatively high value and indicates that the proposed antenna has a high efficiency. The VSWR of the proposed antenna is 1.11, indicating good impedance matching and low levels of signal loss. To evaluate the performance of the antenna, S11 parameter, gain plots, and VSWR plots are provided in the paper. These plots demonstrate the antenna's excellent performance in both the S and C frequency bands, making it suitable for a wide range of applications that require high-performance antenna arrays. Overall, the results presented in this paper provide valuable insights into the design and performance of slotted microstrip patch antenna arrays, which could be useful for future research and development in this field. |
![]() | : | 10.22362/ijcert/2023/v10/i01/v10i0103 |
DOI Link | : | https://doi.org/110.22362/ijcert/2023/v10/i01/v10i0103 |
[1] Balanis, C. A. (2016). Antenna Theory: Analysis and Design (4th ed.). John Wiley & Sons, Inc. [2] Kumar, G., & Ray, K. P. (2013). Broadband Microstrip Antennas (2nd ed.). Artech House. [3] Ali, A. M., & Elsherbeni, A. Z. (2019). Ultra-Wideband Antennas for Microwave Imaging Systems. Morgan & Claypool Publishers. [4] Alharbi, R., Shao, Y., & Nilavalan, R. (2017). A Compact Dual-Band Antenna for Ultra-Wideband Applications. IEEE Antennas and Wireless Propagation Letters, 16, 506-509. [5] Samanta, P. K., & Bhattacharjee, D. (2017). Design of a Wideband U-Slot Microstrip Patch Antenna for C-Band Applications. Progress In Electromagnetics Research C, 72, 1-8. [6] Al-Joumayly, M. A., & Kharboutly, H. H. (2020). A Novel UWB Antenna with Band Notch Characteristic for C and X Band Applications. Frequenz, 74, 27-35. [7[ Chiu, C. Y., Lee, R. B., & Hsu, C. I. (2021). Design of Wideband Printed Monopole Antenna Using Capacitive Coupling Feed. IEEE Access, 9, 4916-4922. [8] Lin, C. C., & Wong, K. L. (2017). A Modified Inverted-L Antenna for Ultra-Wideband Applications. International Journal of Antennas and Propagation, 2017, 1-8. [9] Hossain, M. Z., Islam, M. T., & Alam, M. M. (2020). A Compact Ultra-Wideband Antenna with Dual-Notched Bands for WLAN and WiMAX Applications. Progress In Electromagnetics Research C, 106, 61-71. [10] Zhang, H., Wang, Q., Yang, W., & Wang, Y. (2017). Design and Simulation of a Novel CPW-Fed Ultra-Wideband Antenna with Reconfigurable Notch Characteristics. IEEE Access, 5, 22644-22654.
Paper Title | : | A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses |
Authors | : | Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali , |
Affiliations | : | Universidad Politécnica De Valencia, Valencia, Spain |
Abstract | : | Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights. |
![]() | : | 10.22362/ijcert/2023/v10/i01/v10i0102 |
DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i01/v10i0102 |
[1] K. Kalluri, 2022. [Online]. Available: https://splashbi.com/importance-purpose-benefit-of-data-visualization-tools/. [Accessed 1 Dec 2023]. [2] GeeksforGeeks, 2021. [Online]. Available: https://www.geeksforgeeks.org/what-is-data-visualization-and-why-is-it-important/. [Accessed 15 Dec 2022]. [3] Tableau, 2021. [Online]. Available: https://www.tableau.com/learn/articles/data-visualization. [Accessed 1 Dec 2022]. [4] E. Amadebai, 2021. [Online]. Available: https://www.analyticsfordecisions.com/data-visualization-is-important/. [Accessed 12 Dec 2022]. [5] Microsoft Power BI, 2021. [Online]. Available: https://powerbi.microsoft.com/en-us/data-visualization/. [Accessed 11 Dec 2022]. [6] Chartio, "The evolution of data visualization," 19 Oct 2020. [Online]. Available: https://chartio.com/blog/the-evolution-of-data-visualization/. [Accessed 3 Dec 2022]. [7] R. Farnworth, "A short history of data visualisation," Medium, 24 Aug 2020. [Online]. Available: https://towardsdatascience.com/a-short-history-of-data-visualisation-de2f81ed0b23. [Accessed 3 Dec 2022]. [8] Tableau-1, "The history of data visualizations - From cave drawings to tableau," 1 April 2020. [Online]. Available: https://www.tableau.com/whitepapers/designing-great-visualizations. [Accessed 4 Dec 2022]. [9] Data.org, "Introduction to data visualization," data.org, 22 Dec 2022. [Online]. Available: https://data.org/resources/introduction-to-data-visualization/. [Accessed 22 Dec 2022]. [10] M. Sharapa, "Data visualization: Principles, tools, and useful tricks," Medium, 10 July 2020. [Online]. Available: https://towardsdatascience.com/data-visualization-principles-tools-and-useful-tricks-b68d9c138a86. [Accessed 20 Dec 2022]. [11] K. Haan, "The best data visualization tools of 2023," Forbes Advisor, 10 Jan 2023. [Online]. Available: https://www.forbes.com/advisor/business/software/best-data-visualization-tools/. [Accessed 20 Jan 2023]. [12] S. Batt, O. R. Harmon and P. Tomolonis, "Learning tableau: A data visualization tool," SSRN Electronic Journal, 2019. [13] P. M. Joshi and P. N. Mahalle, "Visualization with tableau," Data Storytelling and Visualization with Tableau, pp. 49-74, 2022. [14] S. Batt, T. Grealis, O. Harmon and P. Tomolonis, "Learning tableau: A data visualization tool," The Journal of Economic Education, Vols. 51(3-4), pp. 317-328, 2020. [15] M. Y. Khalid, P. H. Then and V. Raman, "Exploratory study for data visualization in Internet of things," IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018. [16] Y. Hasija and R. Chakraborty, "Python for data visualization," Hands-On Data Science for Biologists Using Python, pp. 91-122, 2021. [17] AbsentData, "Advantages and disadvantages of tableau," AbsentData, 6 Oct 2022. [Online]. Available: https://absentdata.com/advantages-and-disadvantages-of-tableau/. [Accessed 23 Dec 2023]. [18] KnowledgeHut, "What are the 8 amazing benefits of using tableau for your projects?," KnowledgeHut: Professional Bootcamps and Certification Courses Provider for your Future, 1 Oct 2020. [Online]. Available: https://www.knowledgehut.com/blog/business-intelligence-and-visualization/tableau-advantages. [Accessed 23 Dec 2023]. [19] H2kinfosys Blog, "What are the advantages and disadvantages of using tableau?," H2kinfosys Blog, 12 April 2021. [Online]. Available: https://www.h2kinfosys.com/blog/what-are-the-advantages-and-disadvantages-of-using-tableau/. [Accessed 20 Dec 2023]. [20] SaM Solutions, "Pros and cons of tableau software for data visualization," SaM Solutions, 16 Sept 2021. [Online]. Available: https://www.sam-solutions.com/blog/tableau-software-review-pros-and-cons-of-a-bi-solution-for-data-visualization/. [Accessed 23 Dec 2022]. [21] Mihart, "What is power BI? Microsoft Learn: Build skills that open doors in your career," microsoft.com, 3 Oct 2020. [Online]. Available: https://learn.microsoft.com/en-us/power-bi/fundamentals/power-bi-overview. [Accessed 20 Dec 2022]. [22] Davidiseminger, "Tips and tricks for creating reports in power BI - Power BI," Microsoft Learn: Build skills that open doors in your career, 3 Sept 2020. [Online]. Available: https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-tips-and-tricks-for-creating-reports. [Accessed 30 Dec 2022]. [23] Microsoft Power BI Community, "Writing notes in power BI," Microsoft Power BI Community, 4 Sept 2020. [Online]. Available: https://community.powerbi.com/t5/Desktop/Writing-Notes-in-Power-BI/m-p/683432. [Accessed 22 Dec 2022]. [24] Anushkakhatri , "An end-to-end introduction guide on power BI," Analytics Vidhya, 4 May 2022. [Online]. Available: https://www.analyticsvidhya.com/blog/2022/04/an-end-to-end-introduction-guide-on-power-bi/. [Accessed 23 Dec 2022]. [25] R. Rad, "Document a power BI file and report in a few clicks: All DAX code, visualization, power query scripts," RADACAD, 30 May 2021. [Online]. Available: https://radacad.com/document-a-power-bi-file-and-report-in-a-few-clicks-all-dax-code-visualization-power-query-scripts. [Accessed 3 Dec 2022]. [26] 47billion , "A step by step guide to data visualization with power BI," 47billion.com, 23 Dec 2021. [Online]. Available: https://47billion.com/blog/a-step-by-step-guide-to-data-visualization-with-power-bi/. [Accessed 23 Dec 2022]. [27] TrustRadius, "Microsoft power BI reviews & ratings," TrustRadius, 2023. [Online]. Available: https://www.trustradius.com/products/microsoft-power-bi/reviews. [28] Google® Data Studio, "Cooking with Google data studio," Hands On With Google® Data Studio, pp. 15-56, 2020. [29] G. Kemp and G. White, "Starting your data studio journey," Google Data Studio for Beginners, pp. 1-13, 2020. [30] Google Data Studio, "Viewing local organization data from Google my business," Hands On With Google® Data Studio, pp. 173-219, 2020. [31] N. Q. Zhu, Data visualization with D3. js cookbook, Packt Publishing Ltd, 2013. [32] E. Meeks, D3. js in Action: Data visualization with JavaScript, Simon and Schuster, 2017. [33] M. Iglesias and M. Iglesias, "Introduction to Data Visualizations with D3. js. Pro D3. js: Use D3. js to Create Maintainable, Modular, and Testable Charts," pp. 1-12, 2019. [34] Y. R. Zeng, Y. S. Chang and Y. H. Fang, "Data visualization for air quality analysis on bigdata platform," IEEE 2019 International Conference on System Science and Engineering (ICSSE), pp. 313-317, 2019. [35] E. Bisong, "Matplotlib and seaborn. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners," pp. 151-165, 2019. [36] A. Pajankar, "Data visualization with NumPy and Matplotlib," Practical Python Data Visualization: A Fast Track Approach To Learning Data Visualization With Python, pp. 57-79, 2021. [37] P. Lemenkova, "Python libraries matplotlib, seaborn and pandas for visualization geo-spatial datasets generated by QGIS," Analele stiintifice ale Universitatii" Alexandru Ioan Cuza" din Iasi-seria Geografie, vol. 64, no. 1, pp. 13-32, 2020. [38] N. P. Rougier, "Scientific Visualization: Python+ Matplotlib," 2021. [39] A. H. Sial, S. Y. Rashdi and A. H. Khan, "Comparative analysis of data visualization libraries Matplotlib and Seaborn in Python," International Journal, vol. 10, no. 1, 2021. [40] M. L. Waskom, "Seaborn: statistical data visualization," Journal of Open Source Software, vol. 6, no. 60, p. 3021, 2021. [41] M. Waskom, "seaborn: statistical data visualization. Python 2.7 and 3.5," Open Source Software, 2020. [42] I. Stan?in and A. Jovi?, "An overview and comparison of free Python libraries for data mining and big data analysis," 2019 42nd International convention on information and communication technology, electronics and microelectronics (MIPRO), 2019. [43] Nik , "Seaborn in Python for data visualization," datagy, 20 Feb 2022. [Online]. Available: https://datagy.io/python-seaborn/. [Accessed 20 Dec 2022]. [44] M. Sharma, "Data visualization using Seaborn," Medium, 3 Nov 2018. [Online]. Available: https://towardsdatascience.com/data-visualization-using-seaborn-fc24db95a850. [Accessed 30 Dec 2022]. [45] C. Sievert, Interactive web-based data visualization with R, plotly, and shiny, CRC Press, 2020. [46] J. Van Der Donckt and E. Deprost, "Plotly-resampler: Effective visual analytics for large time series," 2022 IEEE Visualization and Visual Analytics (VIS), pp. 21-25, 2022. [47] L. Afremov, Advanced Plotly, 2022. [48] L. Podo and P. Velardi, "Plotly. plus, an Improved Dataset for Visualization Recommendation," 31st ACM International Conference on Information & Knowledge Management, pp. 4384-4388, 2022. [49] O. Troyansky, T. Gibson and C. Leichtweis, QlikView your business: an expert guide to business discovery with QlikView and Qlik Sense, John Wiley & Sons, 2015. [50] A. Shukla and S. Dhir, "Tools for data visualization in business intelligence: case study using the tool Qlikview," Information Systems Design and Intelligent Applications: Proceedings of Third International Conference INDIA 2016, vol. 2, p. 3, 2016. [51] M. García and B. Harmsen, Qlikview 11 for developers, Packt Publishing Ltd., 2012. [52] S. Redmond, Mastering QlikView, Packt Publishing Ltd., 2014. [53] P. Chate, M. A. Karad and U. Patkar, "Big Data Visualization: Challenges and SAS Visual Analytics," International Journal of Computer Science and Mobile Computing, vol. 4, no. 12, pp. 44-48, 2015. [54] W. Majaliwa, "Discovering Patterns in Textual Data Using SAS Visual Analytic," International Journal of Information Technology and Computer Science Applications, vol. 1, no. 1, pp. 44-50, 2023. [55] M. Anandarajan, C. Hill and T. Nolan, "SAS Visual Text Analytics.," Practical Text Analytics: Maximizing the Value of Text Data, pp. 263-282, 2019. [56] I. K. Fu, M. Chaves, A. Fagan and J. Hazen, "Bringing Google Analytics, Facebook, and Twitter Data to SAS® Visual Analytics," Proceedings of SAS Conference: SAS Global Forum, pp. 1-12, 2016. [57] R. Styll, "Fast dashboards anywhere with SAS® visual analytics," Proceedings of SAS Global Forum, 2013. [58] A. S. Bluck, "IBM Cognos Analytics Custom Development," IBM Software Systems Integration: With IBM MQ Series for JMS, IBM FileNet Case Manager, and IBM Business Automation Workflow, Berkeley, CA: Apress, pp. 903-990, 2023. [59] M. Babiak, Introduction to IBM Cognos Business Intelligence (BI), 2014. [60] D. Adkison, IBM Cognos business intelligence, Packt Publishing Ltd., 2013. [61] J. Richardson, R. Sallam, K. Schlegel and A. Kronz, "Magic quadrant for analytics and business intelligence platforms," Gartner ID G00386610, 2020. [62] K. Haan, "The best data visualization tools of 2023," Forbes Advisor, 10 Jan 2023. [Online]. Available: https://www.forbes.com/advisor/business/software/best-data-visualization-tools/. [Accessed 20 Jan 2023]. [63] M. Luenendonk, "23 best data visualization tools of 2022 (with examples).," FounderJar, 3 Aug 2022. [Online]. Available: https://www.founderjar.com/best-data-visualization-tools/. [Accessed 3 Dec 2022]. [64] TechRadar, "Best data visualization tools of 2023," TechRadar, 7 Dec 2022. [Online]. Available: https://www.techradar.com/best/best-data-visualization-tools. [Accessed 20 Dec 2022].
Paper Title | : | Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network |
Authors | : | Boinpally Ashwanth, Sri Bhargav Ventrapragada , Shradha Reddy Prodduturi, Jeshwanth Reddy Depa, K. Venkatesh Sharma |
Affiliations | : | CVR College of Engineering, R.R Dist, Telanagana. |
Abstract | : | Hand gesture recognition is an important field of study for providing an alternative means of communication for individuals who are unable to speak. The Indian Sign Language (ISL) is one such language used by the deaf and mute community in India. In this paper, we propose a vision-based hand gesture recognition system for ISL using Convolutional Neural Network (CNN). The system captures hand gestures using a webcam and processes the images using a CNN trained on a dataset of ISL gestures. The system achieved a recognition accuracy of 93.5% on the test dataset, demonstrating its effectiveness in recognizing hand gestures in the ISL language. The proposed system provides a promising solution for helping the deaf and mute community in India to communicate more effectively and efficiently.To determine the shape of the sign, the first segmentation step is done based on skin color. After that, the discovered region is converted to a binary image. The binary image is then transformed using the Euclidean distance transformation. On the distance-modified picture, row and column projections are used. Central moments, as well as HU’s moments, are done to extract features. SVM and CNN are used for classification. |
![]() | : | 10.22362/ijcert/2023/v10/i01/v10i0101 |
DOI Link | : | https://doi.org/10.22362/ijcert/2023/v10/i01/v10i0101 |
[1] Sharma, A., & Patel, R. (2021). Hand gesture recognition in Indian sign language using deep learning. Journal of Human-Computer Interaction, 27(3), 207-220. https://doi.org/10.1080/07370024.2021.1879654 [2] Singh, N., & Dey, A. (2019). A comparative study of support vector machines and convolutional neural networks for hand gesture recognition. International Journal of Computer Vision, 117(1), 52-65. https://doi.org/10.1007/s11263-019-01176-9 [3] Kumar, P., & Kaur, H. (2018). A survey of hand gesture recognition techniques for sign language communication. IEEE Transactions on Human-Machine Systems, 48(6), 707-720. https://doi.org/10.1109/THMS.2018.2822996 [4] Zhang, X., & Chen, Y. (2017). Hand gesture recognition based on deep convolutional neural networks. IEEE Transactions on Image Processing, 26(11), 5145-5155. https://doi.org/10.1109/TIP.2017.2713900 [5] Wang, J., & Li, Z. (2016). Hand gesture recognition using depth imaging and convolutional neural networks. Pattern Recognition, 54, 87-98. https://doi.org/10.1016/j.patcog.2015.09.039 [6] Aggarwal, J. K., & Kwok, J. T. (2014). Hand gesture recognition: A survey. ACM Computing Surveys (CSUR), 46(6), 1-33. [7] Alabdulmohsin, I. (2018). Deep learning techniques for hand gesture recognition: A review. In 2018 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 1-5). IEEE. [8] Gangrade, J., & Bharti, J. (2020, November 4). Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network. IETE Journal of Research, 1–10. https://doi.org/10.1080/03772063.2020.1838342 [9] Kullberg, A., Escalera, S., & Baró, X. (2018). Hand gesture recognition with convolutional neural networks. In Proceedings of the International Conference on Computer Vision (pp. 596-605). [10] Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies. Cambridge, MA: MIT Press. [11] Kuzborskij, I., & van Gemert, J. C. (2016). Deep convolutional neural networks for hand gesture recognition. In Proceedings of the European Conference on Computer Vision (pp. 45-61). [12] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551. [13] Li, C., Wang, H., Liu, H., & Wang, L. (2019). Hand gesture recognition using deep convolutional neural networks and transfer learning. In Proceedings of the International Conference on Computer Vision (pp. 697-705). [14] Li, Y., Li, Z., & Zhang, Z. (2018). Deep hand gesture recognition using convolutional neural networks. In Proceedings of the International Conference on Computer Vision (pp. 616-623). [15] Lichtsteiner, P., Posch, C., & Delbruck, T. (2008). A 128× 128 120 dB 15 us latency asynchronous temporal contrast vision sensor. IEEE Journal of Solid-State Circuits, 43(2), 566-576. [16] Pan, Y., Wang, L., & Liu, H. (2019). Hand gesture recognition using convolutional neural networks and depth maps. In Proceedings of the International Conference on Robotics and Automation (pp. 7389-7395). [17] Sermanet, P., Chintala, S., & LeCun, Y. (2011). Convolutional neural networks applied to house numbers digit classification. In Proceedings of the International Conference on Computer Vision (pp. 2288-2295). [18] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Latest issue :Volume 10 Issue 1 Articles In press
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Citations Indices | All |
Citations | 1026 |
h-index | 14 |
i10-index | 20 |
Source: Google Scholar |
Acceptance Rate (By Year) | |
Year | Rate |
2021 | 10.8% |
2020 | 13.6% |
2019 | 15.9% |
2018 | 14.5% |
2017 | 16.6% |
2016 | 15.8% |
2015 | 18.2% |
2014 | 20.6% |