Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10023299
S. Lavanya, G. Paramasivam, G. Maragathavalli
The aim of this study is to do the optical and morphological analysis of chemically synthesized silver nanopar-ticles using nucleation and growth methods as compared with size and absorbance. Chemically synthesized silver nanoparticles were synthesized by silver nitrate with sodium borohydride (N aBH4) in the presence of sodium citrate as capping agent. Here seven groups were analyzed and the total sample size was 26. The nanoparticle was characterized by the means of UV Visible (UV-Vis) spectroscopy and scanning and electron microscopy (SEM).The absorbance and morphology is analysed of the samples. Nanoparticles are having different absorbances in different wavelengths. It is very essential to analyse the absorbance of the nanoparticles. The particle was analysed from 200–700 nm. It has high absorbance in the visible region about 400–420 nm which is in the violet region. The morphology is analysed using SEM to determine the size distribution. It has an average size of 9.5 nm. The morphology (SEM) is taken to analyse the size distribution of the particle.
{"title":"Comparison of Optical and Morphological Analysis of Chemically Synthesized Silver Nanoparticles using Nucleation and Growth method","authors":"S. Lavanya, G. Paramasivam, G. Maragathavalli","doi":"10.1109/MACS56771.2022.10023299","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023299","url":null,"abstract":"The aim of this study is to do the optical and morphological analysis of chemically synthesized silver nanopar-ticles using nucleation and growth methods as compared with size and absorbance. Chemically synthesized silver nanoparticles were synthesized by silver nitrate with sodium borohydride (N aBH4) in the presence of sodium citrate as capping agent. Here seven groups were analyzed and the total sample size was 26. The nanoparticle was characterized by the means of UV Visible (UV-Vis) spectroscopy and scanning and electron microscopy (SEM).The absorbance and morphology is analysed of the samples. Nanoparticles are having different absorbances in different wavelengths. It is very essential to analyse the absorbance of the nanoparticles. The particle was analysed from 200–700 nm. It has high absorbance in the visible region about 400–420 nm which is in the violet region. The morphology is analysed using SEM to determine the size distribution. It has an average size of 9.5 nm. The morphology (SEM) is taken to analyse the size distribution of the particle.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117073505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022769
Muhammad Hasnain Ishtiaq, Faisal Rehman, Nadeem Sarfraz, Hana Sharif, Hira Akram, Haseeb Arshad, Hamid Manzoor
On of the most serious health disease in all over the world is tuberculosis. Tuberculosis is an infectious disease as it affects the human body. According to the World Health Organization, approximately 1.7 million people affected with the tuberculosis during their lifetime. Pakistan is ranked fifth among high-burden countries and accounts for 61% of the tuberculosis burden in the WHO Eastern Mediterranean Region. Different methodology and techniques are available for the early detection of the tuberculosis, but these techniques and methodology have limitations. Majority of the techniques available in literature are using model-based segmentation of lung for the detection of tuberculosis. The main objective of the proposed research is to diagnose pulmonary tuberculosis using chest X- ray (Poster Anterior) lung images with a combination of image processing and machine learning techniques. The proposed research presents novel model segmentation approach for the detection of the tuberculosis. Different deep learning-based approaches are used for the classification like CNN, Google Net etc. The highest accuracy achieved for the proposed approach using Google Net as of 89.58% on combined datasets. The proposed research is helpful for accurate detection and diagnoses of tuberculosis.
{"title":"Deep learning to predict Pulmonary Tuberculosis from Chest Poster Anterior Radiographs of Lungs","authors":"Muhammad Hasnain Ishtiaq, Faisal Rehman, Nadeem Sarfraz, Hana Sharif, Hira Akram, Haseeb Arshad, Hamid Manzoor","doi":"10.1109/MACS56771.2022.10022769","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022769","url":null,"abstract":"On of the most serious health disease in all over the world is tuberculosis. Tuberculosis is an infectious disease as it affects the human body. According to the World Health Organization, approximately 1.7 million people affected with the tuberculosis during their lifetime. Pakistan is ranked fifth among high-burden countries and accounts for 61% of the tuberculosis burden in the WHO Eastern Mediterranean Region. Different methodology and techniques are available for the early detection of the tuberculosis, but these techniques and methodology have limitations. Majority of the techniques available in literature are using model-based segmentation of lung for the detection of tuberculosis. The main objective of the proposed research is to diagnose pulmonary tuberculosis using chest X- ray (Poster Anterior) lung images with a combination of image processing and machine learning techniques. The proposed research presents novel model segmentation approach for the detection of the tuberculosis. Different deep learning-based approaches are used for the classification like CNN, Google Net etc. The highest accuracy achieved for the proposed approach using Google Net as of 89.58% on combined datasets. The proposed research is helpful for accurate detection and diagnoses of tuberculosis.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126637453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022356
N. A, K. Jaisharma, V. Suresh
The objective is to build an efficient face mask detector using Novel YOLOv3. The algorithm used to detect face masks is Novel YOLOv3 in comparison with YOLO, the dataset used was (Facemask Detection Dataset, no date) with the sample size was 136. Novel YOLOv3 gets an accuracy of 92% and in YOLO it is 88% the increase in accuracy is due to the use of Darknet53 neural network model, the novel YOLOv3 and YOLO are statistically satisfied with the independent sample t-test value $(mathrm{P}unicode{x00A1}{0.001})$ with confidence level of 95%. Face Mask detection in Novel Yolov3 has a significantly better accuracy than YOLO.
目的是利用Novel YOLOv3构建一个高效的口罩检测器。与YOLO相比，用于检测口罩的算法是Novel YOLOv3，使用的数据集为(Facemask Detection dataset, no date)，样本量为136。新型YOLOv3的准确率为92%，YOLO的准确率为88%，准确率的提高是由于使用了Darknet53神经网络模型，新型YOLOv3和YOLO在统计上满足独立样本t检验值$( mathm {P}unicode{x00A1}{0.001})$，置信水平为95%。Novel Yolov3的人脸检测准确率明显优于YOLO。
{"title":"An Efficient Automatic Face Mask Detection System for Human Safety Based on Deep Learning using Novel YOLOv3 in Comparison of YOLO with Improved Accuracy","authors":"N. A, K. Jaisharma, V. Suresh","doi":"10.1109/MACS56771.2022.10022356","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022356","url":null,"abstract":"The objective is to build an efficient face mask detector using Novel YOLOv3. The algorithm used to detect face masks is Novel YOLOv3 in comparison with YOLO, the dataset used was (Facemask Detection Dataset, no date) with the sample size was 136. Novel YOLOv3 gets an accuracy of 92% and in YOLO it is 88% the increase in accuracy is due to the use of Darknet53 neural network model, the novel YOLOv3 and YOLO are statistically satisfied with the independent sample t-test value $(mathrm{P}unicode{x00A1}{0.001})$ with confidence level of 95%. Face Mask detection in Novel Yolov3 has a significantly better accuracy than YOLO.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126713563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022844
M. Iswariya, T. Yuvaraj
The two compensators (DSTATCOM and UPQC placement) for improving the voltage profile in the Distribution Network are the subject of a comparative analysis proposed in this research. Innovative DSTATCOM (Distributed Static Compensator) and Unified Power Quality Conditioner (UPQC) are allocated in the Distribution Network for voltage profile improvement. Based on the obtained results, the voltage profile has been improved using DSTATCOM (0.9339 p.u) than UPQC (0.9273 p.u) placement. DSTATCOM gives better voltage profile improvement than UPQC for the selected data set.
{"title":"Voltage profile improvement in distribution networks using DSTATCOM and UPQC by reducing power loss","authors":"M. Iswariya, T. Yuvaraj","doi":"10.1109/MACS56771.2022.10022844","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022844","url":null,"abstract":"The two compensators (DSTATCOM and UPQC placement) for improving the voltage profile in the Distribution Network are the subject of a comparative analysis proposed in this research. Innovative DSTATCOM (Distributed Static Compensator) and Unified Power Quality Conditioner (UPQC) are allocated in the Distribution Network for voltage profile improvement. Based on the obtained results, the voltage profile has been improved using DSTATCOM (0.9339 p.u) than UPQC (0.9273 p.u) placement. DSTATCOM gives better voltage profile improvement than UPQC for the selected data set.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122532524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10023240
Desai Hruthik, Suresh Kumar M, Emg Subramanian
In this paper an innovative microstrip patch antenna is designed to enhance the return loss. This work presents the comparative analysis of the return loss enhancement between the microstrip patch antenna and circular patch antenna. Materials and methods: The return loss of proposed microstrip patch antenna was compared to circular patch antenna in High-frequency structure simulator environment. The sample size estimation is done using the G Power statistical tool with probability of 80% and the total sample size of the research is 20 and consists of two groups i.e for each group 10 samples. From the SPSS analysis the significance is obtained (0.039) which is less than 0.05. Results: The return loss is improved for -40.56 in the proposed innovative microstrip patch antenna in ka-band frequency range compared to circular patch antenna (−12.59 dB). Conclusion: Within the limits of this study, the proposed microstrip patch antenna attained higher return loss in Ka-band. The results are verified using the HFSS modeling.
{"title":"Design of Microstrip Patch Antenna For Ka-band and Comparison of The Return Loss With Circular Patch Antenna","authors":"Desai Hruthik, Suresh Kumar M, Emg Subramanian","doi":"10.1109/MACS56771.2022.10023240","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023240","url":null,"abstract":"In this paper an innovative microstrip patch antenna is designed to enhance the return loss. This work presents the comparative analysis of the return loss enhancement between the microstrip patch antenna and circular patch antenna. Materials and methods: The return loss of proposed microstrip patch antenna was compared to circular patch antenna in High-frequency structure simulator environment. The sample size estimation is done using the G Power statistical tool with probability of 80% and the total sample size of the research is 20 and consists of two groups i.e for each group 10 samples. From the SPSS analysis the significance is obtained (0.039) which is less than 0.05. Results: The return loss is improved for -40.56 in the proposed innovative microstrip patch antenna in ka-band frequency range compared to circular patch antenna (−12.59 dB). Conclusion: Within the limits of this study, the proposed microstrip patch antenna attained higher return loss in Ka-band. The results are verified using the HFSS modeling.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125259929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10023060
G. Sumanth, K. Kanimozhi, Murugesan
The main aim of the project is to recognize faces using the Novel Convolutional Neural Network algorithm in comparison with the Haar Cascade algorithm for the Google AI images dataset. Materials and Methods: Recognition of face is performed using CNN Algorithm (N=10) and Haar Cascade algorithm (N=10). CNN algorithm is a supervised machine learning algorithm. The Haar Cascade algorithm is a simple approach mainly used for classifying. Google AI Image dataset is used for Recognition of face. These samples are divided into two types: training samples (n=52,000(75%)) and test samples (n=17500(25%)). By the help of CNN. Accuracy is calculated for face recognition. Results: The accuracy of face recognition using CNN algorithm is 91.01 % and Haar Cascade algorithm is 85.02 %. There is a significant difference between Adaboost algorithm and Support Vector algorithm with $0.04(mathrm{P}unicode{x00A1}{0.05})$). Conclusion: CNN Algorithm appears to have better accuracy than the Haar Cascade algorithm in recognition face.
{"title":"Face Identity Detection and Recognition using Novel Convolutional Neural Network in Comparison with Haar Cascade to Improve Accuracy *","authors":"G. Sumanth, K. Kanimozhi, Murugesan","doi":"10.1109/MACS56771.2022.10023060","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023060","url":null,"abstract":"The main aim of the project is to recognize faces using the Novel Convolutional Neural Network algorithm in comparison with the Haar Cascade algorithm for the Google AI images dataset. Materials and Methods: Recognition of face is performed using CNN Algorithm (N=10) and Haar Cascade algorithm (N=10). CNN algorithm is a supervised machine learning algorithm. The Haar Cascade algorithm is a simple approach mainly used for classifying. Google AI Image dataset is used for Recognition of face. These samples are divided into two types: training samples (n=52,000(75%)) and test samples (n=17500(25%)). By the help of CNN. Accuracy is calculated for face recognition. Results: The accuracy of face recognition using CNN algorithm is 91.01 % and Haar Cascade algorithm is 85.02 %. There is a significant difference between Adaboost algorithm and Support Vector algorithm with $0.04(mathrm{P}unicode{x00A1}{0.05})$). Conclusion: CNN Algorithm appears to have better accuracy than the Haar Cascade algorithm in recognition face.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131694539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022542
A. V. Reddy, K. Ganapathy, H. Babu
The objective of this study is to design and implement a mobile-controlled 2D printing road lane robot by comparing it with rangoli robot: Arduino, Bluetooth module, motor shield, paint tank, and chassis are the materials used in the construction of 2D printing road lane robot. Arduino ide software is used to code the Arduino board from the computer. The total sample size of 20 is considered for group 1 and group 2. 2D printing road lane robot achieved 35% less standard error, moves 23.77% faster than the rangoli robot and has a significance value of less than 0.0001(¡0.05). In this study, it is found that the 2D printing road lane robot performs better than the rangoli robot in printing lanes on the plane surface.
{"title":"Design and Assembling of a Mobile Controlled 2D Printing Road Lane Drawing Robot for Measuring Speed and Distance in Comparison with Rangoli Robot","authors":"A. V. Reddy, K. Ganapathy, H. Babu","doi":"10.1109/MACS56771.2022.10022542","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022542","url":null,"abstract":"The objective of this study is to design and implement a mobile-controlled 2D printing road lane robot by comparing it with rangoli robot: Arduino, Bluetooth module, motor shield, paint tank, and chassis are the materials used in the construction of 2D printing road lane robot. Arduino ide software is used to code the Arduino board from the computer. The total sample size of 20 is considered for group 1 and group 2. 2D printing road lane robot achieved 35% less standard error, moves 23.77% faster than the rangoli robot and has a significance value of less than 0.0001(¡0.05). In this study, it is found that the 2D printing road lane robot performs better than the rangoli robot in printing lanes on the plane surface.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131725758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10022553
P. Rupeshy, M. K. Mariam Bee, V. Suresh
The aim of study is comparative analysis of two algorithms co-adaptive neuro fuzzy inference system classifiers for better efficiency with adaptive neuro fuzzy inference system for brain tumor detection. Materials and Methods: The data set used for this experiment is taken from Kaggle open access dataset. A total of 20 brain magnetic resonance images are used forco-adaptive neuro fuzzy inference system (Group 1) it is compared with adaptive neuro fuzzy inference system (Group 2). To measure the accuracy 80% of the images are used for training, 10% for testing and 10% for validation. Threshold 0.05 and g-power is 80. The performance analysis is done to validate the better methodology in the SPSS Tool. Result: The initial research using adaptive neuro fuzzy inference system(ANFIS) in detection of brain tumor disease has achieved accuracy of 93% and the proposed system has attained accuracy of 96%. Conclusion: It is concluded that the detection of innovative brain tumor in this view, the diagnosis of brain tumor disease using co-adaptive neuro fuzzy inference system (CANFIS) appears to be with better results compared to adaptive neuro fuzzy inference system (ANFIS).
{"title":"Bandlet transform based brain tumor detection and classification of Magnetic resonance image using Coactive Neuro Fuzzy Inference System in comparison with Adaptive Neuro Fuzzy Inference System classifier","authors":"P. Rupeshy, M. K. Mariam Bee, V. Suresh","doi":"10.1109/MACS56771.2022.10022553","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10022553","url":null,"abstract":"The aim of study is comparative analysis of two algorithms co-adaptive neuro fuzzy inference system classifiers for better efficiency with adaptive neuro fuzzy inference system for brain tumor detection. Materials and Methods: The data set used for this experiment is taken from Kaggle open access dataset. A total of 20 brain magnetic resonance images are used forco-adaptive neuro fuzzy inference system (Group 1) it is compared with adaptive neuro fuzzy inference system (Group 2). To measure the accuracy 80% of the images are used for training, 10% for testing and 10% for validation. Threshold 0.05 and g-power is 80. The performance analysis is done to validate the better methodology in the SPSS Tool. Result: The initial research using adaptive neuro fuzzy inference system(ANFIS) in detection of brain tumor disease has achieved accuracy of 93% and the proposed system has attained accuracy of 96%. Conclusion: It is concluded that the detection of innovative brain tumor in this view, the diagnosis of brain tumor disease using co-adaptive neuro fuzzy inference system (CANFIS) appears to be with better results compared to adaptive neuro fuzzy inference system (ANFIS).","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130523444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10023048
S. Charan, G. Uganya, M. N. Kumar
The main aim of this work is to detect liver cancer by segmentation of magnetic resonance images using Convolutional Neural Network in comparison of accuracy and sensitivity with Support Vector Machine Classifier. Materials and Methods: Group 1 was taken as Convolutional Neural Network and Group 2 was taken as Support Vector Machine. These groups are analyzed by an independent sample T-test with a pretest power of 80% whereas total number of samples N=40. Results: Convolutional Neural Network achieves an accuracy of 96.6025% and sensitivity of 97.61%. Support Vector Machine achieves an accuracy of 86.945% and sensitivity 94.385%. There is a significant difference of 0.036 for accuracy and 0.041 for sensitivity. Conclusion: Convolutional Neural Network achieves significantly better accuracy and sensitivity when compared with Support Vector Machine.
{"title":"Comparison of Accuracy and Sensitivity in Liver Cancer Segmentation of Magnetic Resonance Images using Convolutional Neural Network in Comparison with Support Vector Machine","authors":"S. Charan, G. Uganya, M. N. Kumar","doi":"10.1109/MACS56771.2022.10023048","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023048","url":null,"abstract":"The main aim of this work is to detect liver cancer by segmentation of magnetic resonance images using Convolutional Neural Network in comparison of accuracy and sensitivity with Support Vector Machine Classifier. Materials and Methods: Group 1 was taken as Convolutional Neural Network and Group 2 was taken as Support Vector Machine. These groups are analyzed by an independent sample T-test with a pretest power of 80% whereas total number of samples N=40. Results: Convolutional Neural Network achieves an accuracy of 96.6025% and sensitivity of 97.61%. Support Vector Machine achieves an accuracy of 86.945% and sensitivity 94.385%. There is a significant difference of 0.036 for accuracy and 0.041 for sensitivity. Conclusion: Convolutional Neural Network achieves significantly better accuracy and sensitivity when compared with Support Vector Machine.","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130524609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1109/MACS56771.2022.10023294
D. Aravind, N. Nalini, Emg Subramanian
A Novel Square Microstrip patch antenna has been designed using a Bakelite substrate with a minimum thickness of 1.5mm at 4GHz and compared with an Array antenna using Taconic TLC substrate 1mm at 1.9GHz which can be used in Mobile Communication. Materials and methods: Simulation was performed using FEKO software to find out the Electric field between both the antennas. In accordance with this two sample groups were taken, study group, square microstrip patch antenna $(mathrm{N}=11)$ and control group, Array antenna $(mathrm{N}=11)$. The independent sample T test was performed to find out the Electric field between both the antennas. Results: There is a statistically significant difference $(mathrm{p}=0.002)$ between square microstrip patch antenna and an Array antenna using SPSS software. Conclusion: In this comparative analysis square microstrip patch antenna gave higher Electric field (−9.67 dB) than Array antenna with a Electric field (4.95 dB).
{"title":"Design of Novel Square Microstrip Patch Antenna for Mobile Communication and Comparing with Array Antenna","authors":"D. Aravind, N. Nalini, Emg Subramanian","doi":"10.1109/MACS56771.2022.10023294","DOIUrl":"https://doi.org/10.1109/MACS56771.2022.10023294","url":null,"abstract":"A Novel Square Microstrip patch antenna has been designed using a Bakelite substrate with a minimum thickness of 1.5mm at 4GHz and compared with an Array antenna using Taconic TLC substrate 1mm at 1.9GHz which can be used in Mobile Communication. Materials and methods: Simulation was performed using FEKO software to find out the Electric field between both the antennas. In accordance with this two sample groups were taken, study group, square microstrip patch antenna $(mathrm{N}=11)$ and control group, Array antenna $(mathrm{N}=11)$. The independent sample T test was performed to find out the Electric field between both the antennas. Results: There is a statistically significant difference $(mathrm{p}=0.002)$ between square microstrip patch antenna and an Array antenna using SPSS software. Conclusion: In this comparative analysis square microstrip patch antenna gave higher Electric field (−9.67 dB) than Array antenna with a Electric field (4.95 dB).","PeriodicalId":177110,"journal":{"name":"2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133010218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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