Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Singh, Harpreet
- On development of Chip to Control Laser Time for Cell-selective Arrhythmia Ablation of Heart
Abstract Views :114 |
PDF Views:0
Authors
Anubhav Sharma
1,
Shashank Kamthan
1,
Aby K. George
1,
Amjad Almatrood
1,
Harpreet Singh
1,
Harinder Pal Singh
2
Affiliations
1 College of Engineering, Wayne State University, Detroit, MI, US
2 Dept of Technical Education, Government of Punjab, IN
1 College of Engineering, Wayne State University, Detroit, MI, US
2 Dept of Technical Education, Government of Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 17, No 1 (2016), Pagination: 334-342Abstract
Human heart is a very important part of body. Its proper functioning is extremely important for the survival of human being. Quite often, the heart does not function properly owing to one reason or the other. The primary reason for such a malfunctioning of heart could be due to some of the faulty and abnormal cells in heart. Generally, the photo-techniques are used to destroy such abnormal cells. However, such techniques are not much effective. Currently, laser techniques are used to destroy malfunctioning cells of the heart. The basic idea about these techniques is that with the help of a laser beam, some particles are sent to the cells of heart, so that only the abnormal cells get destroyed while retaining the normal or healthy cells of the heart. Further, the paper also demonstrates a procedure for applying a laser beam for a specified time. The procedure is developed in Verilog software. The Verilog program is then implemented on Field Programmable Gate Array (FPGA), and the testing of this program is done. The developed circuit in the present paper is expected to be useful for a number of applications indifferent industries.Keywords
Digital Chip, MATLAB, HDL, Cadence, FPGA, Laser Beam, Ablation of Heart.Full Text
- On the development of Arithmetic Processors
Abstract Views :121 |
PDF Views:0
Authors
Affiliations
1 College of Engineering, Wayne State University, Detroit, MI, US
2 Dept of Technical Education, Government of Punjab, IN
1 College of Engineering, Wayne State University, Detroit, MI, US
2 Dept of Technical Education, Government of Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 17, No 1 (2016), Pagination: 343-348Abstract
There has always been anincreasing interest in the development of new arithmetic processors. The objective of this paper is to describe the hardware implementation of a pipelined arithmetic processorpublished previously, which can add, subtract,multiply,divide,square and square ischolar_main the binary numbers. The processor described resulted in 46input/output pins.The MOSIS and cadence fabrication technology generally allow up to 40 pins. In this paper hardware implementation of arithmetic processor is taken up so that processor chip can be developed by 40 pins. This hardware implementation will lead to better implementation of handling input, output pins for future processors. The hardware implementation of the modified array has been done using Simulink and tested. It is hoped that this research will lead to the design and VLSI implementation of new arithmetic processor.Keywords
Arithmetic processor, Digital Chip, MATLAB, Pipeline Array, Simulink.Full Text
- Development of a Fuzzy Chip for Predicting the Confidence Level of Soldiers in the Army Vehicle
Abstract Views :110 |
PDF Views:0
Authors
Affiliations
1 Department of Mechanical Engineering, Wayne State University, Detroit, MI, US
2 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, US
3 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, IN
4 Department of Technical Education, Government of Punjab, IN
1 Department of Mechanical Engineering, Wayne State University, Detroit, MI, US
2 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, US
3 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, IN
4 Department of Technical Education, Government of Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 20 (2016), Pagination: 172-176Abstract
The army vehicles have to go to different ranges and different environmental conditions as compared to the conventional vehicles. The safety of soldiers driving army vehicles is a big challenge. It is important to continuously monitor the conditions of the environment so as to give confidence to the soldiers whether they feel safe under those conditions. A fuzzy model is developed to predict the soldiers' confidence. A simplified version of the model is implemented using a fuzzy logic toolbox. The procedure for the development of such a chip is given in this paper. It is hoped that such chips, when put on the dashboard of the army vehicle will guide and advise the soldiers for the upcoming conditions.Keywords
Army Vehicles, Soldiers Confidence Metrics, Fuzzy Logic, Matlab Simulations, Soldier Safety.Full Text
- Neuro Fuzzy Logic Model for Component Based Software Engineering
Abstract Views :113 |
PDF Views:0
Authors
Source
Research Cell: An International Journal of Engineering Sciences, Vol 1 (2011), Pagination: 303-314Abstract
Fuzzy logic has proved its mettle in last few decades and has been used in various applications to improve the performance and embeds some intelligence into the system. Fuzzy logic also solves the problem of non linear systems and handles them with great efficiency and provides robustness to the system. However, our aim always lies in achieving the improved solution and there are different hybrid algorithms. In this paper, the recent data based artificially intelligent techniques like Fuzzy have been customized and used .The application/case study has been taken from a research paper which appeared in a reputed general. The case study deals with reusability of software components. The attributes are coupling, volume, complexity, regularity and reuse frequency. In such data search application the design and developed Neuro-fuzzy hybrid algorithm has shown its superiority because it includes the advantages of Fuzzy as well as neural networks. Neuro -fuzzy algorithms is definitely superior to Fuzzy algorithm as it inherits adaptability and learning. From the simulation and the result obtained, it has been shown that the percentage average error is less in Neuro-fuzzy model. Neuro-fuzzy algorithm has yielded accuracy greater than the accuracy levels as in the case of Fuzzy logic for software reusability.Keywords
Fuzzy Logic, Neuro-Fuzzy, Software Reusability.Full Text
- COVID-19 Severity Analysis Using Improved Machine Learning Algorithm
Abstract Views :130 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 11-19Abstract
The new pandemic produced by the COVID-19 virus has resulted in an overflow of medical treatment in clinical centers all over the world. The fast and exponential growth in the number of COVID-19-infected individuals has necessitated an effective and timely prediction of probable infections and their effects in order to reduce health-care quality overload. As a result, intelligent models are being developed and used to assist medical workers in making more accurate diagnoses concerning the health condition of COVID-19-infected individuals. The purpose of this research is to present an alternative algorithmic approach for predicting the health status of COVID-19 patients in Mexico. Different prediction models were assessed and compared, including Adaboost, gradient boosting machine, random forests, and light gradient boosting machine. Additionally, Grid search hyperparameter optimization is used to improve the algorithm's success rate. The optimal model feature analysis procedure is being carried out. The purpose of this study is to analyses features in terms of feature importance as indicated by SHapely adaptive exPlanations (SHAP) values in order to identify relevant predictive factors that can identify patients at high risk of mortality.Keywords
Machine Learning, COVID-19, Hyperparameter Tuning, SHAP Analysis.Full Text
References
- Albataineh, Zaid, Fatima Aldrweesh, and Mohammad A. Alzubaidi. 2023. “COVID-19 CT-Images Diagnosis and Severity Assessment Using Machine Learning Algorithm.” Cluster Computing 5(May 2022).
- Attallah, Omneya. 2022. “An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.” Biosensors 12(5).
- Ciotti, Marco et al. 2020. “The COVID-19 Pandemic.” Critical Reviews in Clinical Laboratory Sciences 0(0): 365–88. https://doi.org/10.1080/10408363.2020.1783198.
- El-Kenawy, El Sayed M. et al. 2020. “Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.” IEEE Access 8.
- Gupta, Subhash Chandra, and Noopur Goel. 2023. “Predictive Modeling and Analytics for Diabetes Using Hyperparameter Tuned Machine Learning Techniques.” Procedia Computer Science 218(2022): 1257–69. https://doi.org/10.1016/j.procs.2023.01.104.
- Kassania, Sara Hosseinzadeh et al. 2021. “Automatic Detection of Coronavirus Disease (COVID-19) in X-Ray and CT Images: A Machine Learning Based Approach.” Biocybernetics and Biomedical Engineering 41(3): 867–79.
- Kini, Anita S. et al. 2022. “Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.” Contrast Media and Molecular Imaging 2022.
- Li, Jifang, Genxu Li, Chen Hai, and Mengbo Guo. 2022. “Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm.” IEEE Access 10: 1522–32.
- Madoery, Pablo G, Ramiro Detke, Lucas Blanco, and Sandro Comerci. 2020. “Since January 2020 Elsevier Has Created a COVID-19 Resource Centre with Free Information in English and Mandarin on the Novel Coronavirus COVID- 19 . The COVID-19 Resource Centre Is Hosted on Elsevier Connect , the Company ’ s Public News and Information .” (January).
- Mansbridge, Nicola et al. 2018. “Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.” Sensors (Switzerland) 18(10): 1–16.
- Ndwandwe, Duduzile, and Charles S. Wiysonge. 2021. “COVID-19 Vaccines.” Current Opinion in Immunology 71(Figure 1): 111–16. https://doi.org/10.1016/j.coi.2021.07.003.
- Patel, Dhruv et al. 2021. “Machine Learning Based Predictors for COVID-19 Disease Severity.” Scientific Reports 11(1): 1–7. https://doi.org/10.1038/s41598-021-83967-7.
- Rostami, Mehrdad, and Mourad Oussalah. 2022. “A Novel Explainable COVID-19 Diagnosis Method by Integration of Feature Selection with Random Forest.” Informatics in Medicine Unlocked 30(January): 100941. https://doi.org/10.1016/j.imu.2022.100941.
- Shekar, B. H., and Guesh Dagnew. 2019. “Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data.” 2019 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019 (November): 1–8.
- Siji George, C. G., and B. Sumathi. 2020. “Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction.” International Journal of Advanced Computer Science and Applications 11(9): 173–78.
- Sreedharan, Radhika, and Archana Praveen Kumar. 2020. “Analysis and Prediction of Smart Data Using Machine Learning.” AIP Conference Proceedings 2240(Ml): 15–21.
- Uddin, Shahadat, Arif Khan, Md Ekramul Hossain, and Mohammad Ali Moni. 2019. “Comparing Different Supervised Machine Learning Algorithms for Disease Prediction.” BMC Medical Informatics and Decision Making 19(1): 1–16.
- Velavan, Thirumalaisamy P., and Christian G. Meyer. 2020. “The COVID-19 Epidemic.” Tropical Medicine and International Health 25(3): 278–80.
- Wang, Wenyang, and Dongchu Sun. 2021. “The Improved AdaBoost Algorithms for Imbalanced Data Classification.” Information Sciences 563: 358–74. https://doi.org/10.1016/j.ins.2021.03.042.
- https://www.kaggle.com/marianarfranklin/mexico-covid19-clinical-data/
- COVID-19 Diagnosis Using Machine Learning
Abstract Views :123 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 107-113Abstract
Over 4 million individuals have already died as a result of the deadly contagious viral COVID-19 worldwide. The infection can seriously harm the lungs, increasing the chance of fatal health effects. The only way to lower the mortality rate due to this deadly illness and to halt its growth is through early detection. Deep learning has recently come to light as one of the most useful methods for computer aided diagnosis for helping clinicians make correct illness diagnoses. However, deep learning models require a lot of processing, so hardware with TPUs and GPUs is required to execute these models. To create machine learning models that can be used on mobile and peripheral devices, experts are currently working. In this context, the goal of this study is to create a concise Convolution Neural Network-based computer-aided diagnostic system that can be used on devices with limited processing capacity, such as mobile phones and iPads, to identify the presence of the Covid-19 virus in x-ray pictures. On the basis of various assessment parameters, the findings plainly show that the suggested model outperforms other transfer learning models such as Resnet50, Inception, and Xception. According to various evaluation parameters, the findings definitely show that the proposed model outperforms other transfer learning models like Resnet50, Inception, and Xception.Keywords
Deep Learning, CNN, COVID-19, Transfer Learning, Image Enhancement.Full Text
References
- S. Sharma, M.Sharma and G. Singh, “A chaotic and stressed environment for 2019-nCoV suspected, infected and other people in India: Fear of mass destruction and causality,” Asian J. Psychiatry, vol. 51, p. 102049, 2020. doi:10.1016/j.ajp.2020.102049.
- C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, & Z. Xie, “Deep learning and its applications in biomedicine,” Genomics Proteomics Bioinformatics, vol. 16, no. 1, pp. 17-32, 2018.
- J. N. Kanji , “False negative rate of COVID-19 PCR testing: A discordant testing analysis,” Virol. J., vol. 18, no. 1, pp. 1-6, 2021. doi:10.1186/s12985-021-01489-0.
- Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” Lect. Notes Comput. Sci. (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7700 LECTU, pp. 437-478, 2012.
- L. Liu, J. Xu, Y. Huan, Z. Zou, S. C. Yeh, & L. R. Zheng, “A smart Dental Health-IoT platform based on intelligent hardware, deep learning, and mobile terminal,” IEEE J. Biomed. Health Inform., vol. 24, no. 3, pp. 898-906, 2020. doi:10.1109/JBHI.2019.2919916.
- A. Maier, C. Syben, T. Lasser, & C. Riess, “A gentle introduction to deep learning in medical image processing,” Z. Med. Phys., vol. 29, no. 2, pp. 86-101, 2019. doi:10.1016/j.zemedi.2018.12.003.
- A. Krizhevsky, I. Sutskever, & G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84-90, 2017.
- M. Toğaçar , B. Ergen, Z. Cömert, & F. Özyurt, “A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models,” IRBM, vol. 41, no. 4, pp. 212-222, 2020. doi:10.1016/j.irbm.2019.10.006.
- T. Chitnis, B. I. Glanz, C. Gonzalez, B. C. Healy, T. J. Saraceno, N. Sattarnezhad, C. D. Cruz, M. P. Turcsanyi, “Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis,” npj Digit. Med., vol. 2, article number 123, 2019. doi:10.1038/s41746-019-0197-7.
- G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, , M. Ghafoorian & C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60-88, 2017. doi:10.1016/j.media.2017.07.005.
- D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S.L. Baxter, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, p. 1122-1131.e9, 2018. doi:10.1016/j.cell.2018.02.010.
- C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, & Z. Xie, “Deep learning and its applications in biomedicine,” Genomics Proteomics Bioinformatics, vol. 16, no. 1, pp. 17-32, 2018. doi:10.1016/j.gpb.2017.07.003.
- A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, & J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Meas. J. Int. Meas. Confed ., vol. 145, pp. 511-518, 2019.
- D. Singh, V. Kumar, Vaishali & M. Kaur, “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks,” Eur. J. Clin. Microbiol. Infect. Dis., vol. 39, no. 7, pp. 1379-1389, 2020. doi:10.1007/s10096-020-03901-z.
- N. Narayan Das, N. Kumar, M. Kaur, V. Kumar & D. Singh, “Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays,” IRBM, 2020. doi:10.1016/j.irbm.2020.07.001.
- L. Brunese, F. Mercaldo, A. Reginelli, & A. Santone, “Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays,” Comput. Methods Programs Biomed., vol. 196, p. 105608, 2020. doi:10.1016/j.cmpb.2020.105608.
- R. Patel and A. Chaware, “Transfer learning with fine-tuned MobileNetV2 for diabetic retinopathy.” International Conference for Emerging Technology, INCET 2020, vol. 2020, 2020, pp. 6-9.
- M. N. Wahab , A. Nazir, A.T.Z. Ren, M.H.M. Noor, M.F. Akbar, and A.S.A. Mohamed, “Efficientnet-lite and hybrid CNN-KNN implementation for facial expression recognition on raspberry pi,” IEEE Access, vol. 9, pp. 134065-134080, 2021.
- Available at: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
- D. Ezzat and H. A. Ella, “An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization,” Appl. Soft Comput., vol. 98, p. 106742, 2021. doi:10.1016/j.asoc.2020.106742.