Pub Date : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971993
S. Kanagaraj, M. Hema, M. Guptha, V. Namitha
The non-curable neurological disorder that affects the motor system is known as Parkinson disease. When Parkinson disease is detected earlier, then it can diagnose, and we can get a quick relief but not permanent. The neurons segregate a chemical called dopamine. That helps for transmitting the signs to the other neurons in the brain. When the dopamine flow starts to fall, then the PD occurs. This makes the patients to, resting tremors, bradykinesia and rigidity problems. Here machine-learning dramatizations position in patterns tag in biomedical sciences. The PD mainly attack the motor system so that can be analysed by the Magnetic Resonance Imaging (MRI) scan, one can detect and predict the disease. In this paper, with MRI scan the Parkinson's disease is detected by using CNN, VGG-16 model and ResNET-50. The VGG-16 and ResNet-50 are compared and find the best model based on the accuracy.
{"title":"Detecting Parkinson's Disease with Image Classification","authors":"S. Kanagaraj, M. Hema, M. Guptha, V. Namitha","doi":"10.1109/GCAT55367.2022.9971993","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971993","url":null,"abstract":"The non-curable neurological disorder that affects the motor system is known as Parkinson disease. When Parkinson disease is detected earlier, then it can diagnose, and we can get a quick relief but not permanent. The neurons segregate a chemical called dopamine. That helps for transmitting the signs to the other neurons in the brain. When the dopamine flow starts to fall, then the PD occurs. This makes the patients to, resting tremors, bradykinesia and rigidity problems. Here machine-learning dramatizations position in patterns tag in biomedical sciences. The PD mainly attack the motor system so that can be analysed by the Magnetic Resonance Imaging (MRI) scan, one can detect and predict the disease. In this paper, with MRI scan the Parkinson's disease is detected by using CNN, VGG-16 model and ResNET-50. The VGG-16 and ResNet-50 are compared and find the best model based on the accuracy.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115482203","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972053
N. Nimbarte, Aniket Nagpure, Badal Sanodiya, Harshal Sevatkar, S. Balamwar
Pattern Recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The Image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis.
{"title":"Knowledge Based Classifier and Pattern Recognition Technique for Satellite Image Analysis","authors":"N. Nimbarte, Aniket Nagpure, Badal Sanodiya, Harshal Sevatkar, S. Balamwar","doi":"10.1109/GCAT55367.2022.9972053","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972053","url":null,"abstract":"Pattern Recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The Image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"13 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124688462","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972044
K. S. R. Kumar, H. K. Ravi
To Monitor and analyze vibration profile of BS-6 vehicle engine system. BS-6 is the sixth emission benchmark (or) criteria in terms of lowering pollution levels in comparison to BS-4 emission, Currently there are no cost-effective high speed vibration analyzers and loggers readily available in the open market. Aim is to target OEM's like Robert Bosch to help them to capture 3 - axis 3 - channel vibration sensor data along with GPS and logging data at a expected speed of 5.3k samples per second. Where Automotive OEMs depend on noise, harshness, and vibration testing to improve vehicle performance and to maintain a level of comfort throughout the entire vehicle lineup. The vibration occurs in the X,Y,Z directions 3-axis control ensures that the device can withstand vibration reflective of the field environment becomes of such, Many test standards have begun to include 3-axis vibration testing. 3-axis testing is primarily used for component or subsystem testing. It is accomplished by running a random vibration test along each axis using identical or individualized test profiles. It creates a more realistic test compared to traditional single-axis testing.
{"title":"Automotive vibration datalogger","authors":"K. S. R. Kumar, H. K. Ravi","doi":"10.1109/GCAT55367.2022.9972044","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972044","url":null,"abstract":"To Monitor and analyze vibration profile of BS-6 vehicle engine system. BS-6 is the sixth emission benchmark (or) criteria in terms of lowering pollution levels in comparison to BS-4 emission, Currently there are no cost-effective high speed vibration analyzers and loggers readily available in the open market. Aim is to target OEM's like Robert Bosch to help them to capture 3 - axis 3 - channel vibration sensor data along with GPS and logging data at a expected speed of 5.3k samples per second. Where Automotive OEMs depend on noise, harshness, and vibration testing to improve vehicle performance and to maintain a level of comfort throughout the entire vehicle lineup. The vibration occurs in the X,Y,Z directions 3-axis control ensures that the device can withstand vibration reflective of the field environment becomes of such, Many test standards have begun to include 3-axis vibration testing. 3-axis testing is primarily used for component or subsystem testing. It is accomplished by running a random vibration test along each axis using identical or individualized test profiles. It creates a more realistic test compared to traditional single-axis testing.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869257","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972211
Gnaneswari G
Medical data is made up of a huge number of heterogeneous variables gathered from various sources all of which provide a different perspective on a patient's condition. Machine Learning proves to be very effective method for the prediction of unstructured data. Algorithms such as SVC, K Nearest Neighbor, Random Forest Classifier, Naïve Bayes etc. can be used for the early detection for the disease. Data mining technique are used to gather data from health care databases and are used for making clinical decision of the disease at the preliminary level without the intervention of the medical experts.[1] Using the state-of-the-art wearable electronic equipment can also be used for collecting continuous data from the patients. The classification techniques in the area of Machine Learning in the medical field, with the goal to find similar patterns, thereby producing vital predictions, and being useful in early diagnosis of the disease is the focus of this research paper. The algorithm which fits the data and predicts with more accuracy is analyzed. The novelty in this research is predicting if a patient already with a heart disease will get a heart attack or not. Whereas, most of the researchers are interested only in predicting the presence of a heart disease. This paper focuses on the prediction of heart attacks in patients having a heart disease.
{"title":"Analysis of The Diagnostic Parameters of Heart Diseases and Prediction of Heart Attacks","authors":"Gnaneswari G","doi":"10.1109/GCAT55367.2022.9972211","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972211","url":null,"abstract":"Medical data is made up of a huge number of heterogeneous variables gathered from various sources all of which provide a different perspective on a patient's condition. Machine Learning proves to be very effective method for the prediction of unstructured data. Algorithms such as SVC, K Nearest Neighbor, Random Forest Classifier, Naïve Bayes etc. can be used for the early detection for the disease. Data mining technique are used to gather data from health care databases and are used for making clinical decision of the disease at the preliminary level without the intervention of the medical experts.[1] Using the state-of-the-art wearable electronic equipment can also be used for collecting continuous data from the patients. The classification techniques in the area of Machine Learning in the medical field, with the goal to find similar patterns, thereby producing vital predictions, and being useful in early diagnosis of the disease is the focus of this research paper. The algorithm which fits the data and predicts with more accuracy is analyzed. The novelty in this research is predicting if a patient already with a heart disease will get a heart attack or not. Whereas, most of the researchers are interested only in predicting the presence of a heart disease. This paper focuses on the prediction of heart attacks in patients having a heart disease.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121742873","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972177
Smitha R Vasisth, Swathi P N, T. N. Kusuma, H. S. Pradeep
As the need for 5G connectivity has grown, millimeter-wave (mm-Wave) wireless technology has been extensively used. Since there is so much available bandwidth, mm-Wave bands are more dependable for higher data rates. The frequency ranges of 28 GHz and 38 GHz are two of the most promising options for fifth-generation communication among the various mmWave bands putout. In millimeter-wave mobile communication, circularly polarized (CP) antennas are greatly desired in order to minimize the delay spread in a multipath propagation. The suggested antenna has circular polarisation and is designed to work at two center frequencies (28/38 GHz). The antenna has L-shaped slots loaded over the patch at its borders, which allows it to operate in two bands and have circular polarisation. The antenna is implemented on a FR-4 substrate with a loss tangent of 0.025, a substrate height of 0.8 mm, and a relative permittivity of 4.3. CST Microwave Studio's electromagnetic simulation software is used to implement the design. The antenna is 8 x 8 mm2in size overall. It is possible to achieve peak gains of 5.4 dBi and 5.25 dBi at 27.8 GHz and 39.6 GHz, respectively. The antenna's prototype is fabricated and tested for validation. Since the simulated and measured results are in close agreement, the antenna is suitable for 5G communication.
{"title":"Design of Miniaturized, Circularly Polarized, Dual Band mm-Wave Antenna for 5G Communication","authors":"Smitha R Vasisth, Swathi P N, T. N. Kusuma, H. S. Pradeep","doi":"10.1109/GCAT55367.2022.9972177","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972177","url":null,"abstract":"As the need for 5G connectivity has grown, millimeter-wave (mm-Wave) wireless technology has been extensively used. Since there is so much available bandwidth, mm-Wave bands are more dependable for higher data rates. The frequency ranges of 28 GHz and 38 GHz are two of the most promising options for fifth-generation communication among the various mmWave bands putout. In millimeter-wave mobile communication, circularly polarized (CP) antennas are greatly desired in order to minimize the delay spread in a multipath propagation. The suggested antenna has circular polarisation and is designed to work at two center frequencies (28/38 GHz). The antenna has L-shaped slots loaded over the patch at its borders, which allows it to operate in two bands and have circular polarisation. The antenna is implemented on a FR-4 substrate with a loss tangent of 0.025, a substrate height of 0.8 mm, and a relative permittivity of 4.3. CST Microwave Studio's electromagnetic simulation software is used to implement the design. The antenna is 8 x 8 mm2in size overall. It is possible to achieve peak gains of 5.4 dBi and 5.25 dBi at 27.8 GHz and 39.6 GHz, respectively. The antenna's prototype is fabricated and tested for validation. Since the simulated and measured results are in close agreement, the antenna is suitable for 5G communication.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122085109","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971867
Digil Biji George, R. K. Megalingam
Pharmaceuticals is an industry of paramount importance considering the present day scenarios taking into account the evolving variants of the COVID-19 virus. It holds the cards in diagnosis, control and treatment of any disease. Therefore accuracy and precision of the medicinal administration is critical in treatment. The revolutionary and immeasurable capabilities and potential of application of robotics in health-care industry have proven to attain perfection and raise the bar of efficiency and quality of health-care theoretically and practically worldwide. The interdisciplinary characters of robotics have contributed limitless dominance in the field broadening the quality standards and also expedite the discovery and delivery process of specific drug formulary. The objective of this project is to design and develop a prototype of drug delivery robot that fetches the specified drug from the stockpile that have been categorized appropriately by adopting a simple pick-and-place operation by the robot to fetch any drug that has been specified to it from the source of storage. The input specified will be the prescribed medicine and the robot autonomously navigates fetches the medicine from the source of storage.
{"title":"Autonomous Pharmaceutical Dispensing Robot","authors":"Digil Biji George, R. K. Megalingam","doi":"10.1109/GCAT55367.2022.9971867","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971867","url":null,"abstract":"Pharmaceuticals is an industry of paramount importance considering the present day scenarios taking into account the evolving variants of the COVID-19 virus. It holds the cards in diagnosis, control and treatment of any disease. Therefore accuracy and precision of the medicinal administration is critical in treatment. The revolutionary and immeasurable capabilities and potential of application of robotics in health-care industry have proven to attain perfection and raise the bar of efficiency and quality of health-care theoretically and practically worldwide. The interdisciplinary characters of robotics have contributed limitless dominance in the field broadening the quality standards and also expedite the discovery and delivery process of specific drug formulary. The objective of this project is to design and develop a prototype of drug delivery robot that fetches the specified drug from the stockpile that have been categorized appropriately by adopting a simple pick-and-place operation by the robot to fetch any drug that has been specified to it from the source of storage. The input specified will be the prescribed medicine and the robot autonomously navigates fetches the medicine from the source of storage.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126281798","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971843
K. Chaitanya, B. Mallikarjun
The requirement for IT infrastructure is continually changing, and new solutions are being developed as a result. Here in this scenario, the existing is referred as an old router and proposed is referred to new router configuration as an illustrative example. Hence there is a need for migration. In this work, A detailed network service migration strategy has been discussed, presented, and realized over real time scenario. This migration strategy requires a detailed, step-by-step methodology. Planning migration steps with several phases is the next and most crucial step. Successful migration requires level of planning. Logically, we are configuring the ports while physically disconnecting the fiber from router a and patching it to router b. The next phase is documentation, which includes in service notification (ISN) and performance reports. We'll verify the new router's performance after the migration to see whether it has any delays or packet losses that are lower than those of router A.
{"title":"A Service Migration Strategy for Communication Networks","authors":"K. Chaitanya, B. Mallikarjun","doi":"10.1109/GCAT55367.2022.9971843","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971843","url":null,"abstract":"The requirement for IT infrastructure is continually changing, and new solutions are being developed as a result. Here in this scenario, the existing is referred as an old router and proposed is referred to new router configuration as an illustrative example. Hence there is a need for migration. In this work, A detailed network service migration strategy has been discussed, presented, and realized over real time scenario. This migration strategy requires a detailed, step-by-step methodology. Planning migration steps with several phases is the next and most crucial step. Successful migration requires level of planning. Logically, we are configuring the ports while physically disconnecting the fiber from router a and patching it to router b. The next phase is documentation, which includes in service notification (ISN) and performance reports. We'll verify the new router's performance after the migration to see whether it has any delays or packet losses that are lower than those of router A.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125954198","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972026
S. Unnikrishnan, S. Gokul Krishna, S. Krishna
loT-based systems can be seen in different areas like healthcare, transportation, farming, the power infrastructure, and manufacturing. Even though the Internet of Things can make people's lives easier, its exponential growth makes it a popular target for cyber-criminals and is subject to significant threats. One of the most devastating attacks is the denial of service (DoS), which prevents legitimate users from accessing services they have paid for. Therefore, There is an urgent requirement of loT-specific intrusion detection systems to tackle all these cyber-attacks. Numerous lightweight protocols are there to secure the communication between the loT devices. Here we used the IDS data set of the most critical loT communication protocol known as Message Queuing Telemetry Transport (MQTT) The information will be used as the foundation for developing creative intrusion detection method in loT networks. This work focused on developing a framework to compare several machine learning algorithms, and display the performance result of each one. The result demonstrated the most accurate model and the importance of using the machine learning-based IDS.
{"title":"A Framework For Comparing Different Machine Learning Algorithm Models For Intrusion Detection In loT Environment","authors":"S. Unnikrishnan, S. Gokul Krishna, S. Krishna","doi":"10.1109/GCAT55367.2022.9972026","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972026","url":null,"abstract":"loT-based systems can be seen in different areas like healthcare, transportation, farming, the power infrastructure, and manufacturing. Even though the Internet of Things can make people's lives easier, its exponential growth makes it a popular target for cyber-criminals and is subject to significant threats. One of the most devastating attacks is the denial of service (DoS), which prevents legitimate users from accessing services they have paid for. Therefore, There is an urgent requirement of loT-specific intrusion detection systems to tackle all these cyber-attacks. Numerous lightweight protocols are there to secure the communication between the loT devices. Here we used the IDS data set of the most critical loT communication protocol known as Message Queuing Telemetry Transport (MQTT) The information will be used as the foundation for developing creative intrusion detection method in loT networks. This work focused on developing a framework to compare several machine learning algorithms, and display the performance result of each one. The result demonstrated the most accurate model and the importance of using the machine learning-based IDS.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126046172","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972133
V. Verma, D. A. K. M. Vaithivanathan, B. Kaur
In this paper, a less complex and single-phase clock flip-flop has been proposed. This flip-flop design is a modified version of the Novel Low-Complexity and Low-Power flip-flop. In the design of this flip-flop, a master-slave logic structure is used, representing a hybrid logic design consisting of both past transistor logic (PTL) and complementary metal-oxide-semiconductor (CMOS) logic. The proposed design comprises 15 transistors, two transistors less than the Low-complexity and low power flip-flop. The transistor reduction in this flip-flop is achieved by applying a logic structure reduction technique which also improves the power and timing performance of the design. The proposed plan is implemented on the 90 nm CMOS technology. The proposed plan is implemented on the 90 nm CMOS technology in Cadence Virtuoso tool. It is 48.13%, 30.94% and 22.71% of power saved as compared to TGFF, TCFF and LCLPFF respectively at 1 Volt power supply.
{"title":"Review of Different Flip-Flop Circuits and a Modified Flip-Flop Circuit for Low Voltage Operation","authors":"V. Verma, D. A. K. M. Vaithivanathan, B. Kaur","doi":"10.1109/GCAT55367.2022.9972133","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972133","url":null,"abstract":"In this paper, a less complex and single-phase clock flip-flop has been proposed. This flip-flop design is a modified version of the Novel Low-Complexity and Low-Power flip-flop. In the design of this flip-flop, a master-slave logic structure is used, representing a hybrid logic design consisting of both past transistor logic (PTL) and complementary metal-oxide-semiconductor (CMOS) logic. The proposed design comprises 15 transistors, two transistors less than the Low-complexity and low power flip-flop. The transistor reduction in this flip-flop is achieved by applying a logic structure reduction technique which also improves the power and timing performance of the design. The proposed plan is implemented on the 90 nm CMOS technology. The proposed plan is implemented on the 90 nm CMOS technology in Cadence Virtuoso tool. It is 48.13%, 30.94% and 22.71% of power saved as compared to TGFF, TCFF and LCLPFF respectively at 1 Volt power supply.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129375127","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971947
Jivesh Singh, Gurpreet Kaur, Nitika Kapoor
The global burden of mental ailments continues to rise, posing serious health risks as well as huge social, human rights, and economic ramifications in every country. One such disorder is the attention deficit hyperactivity disorder (ADHD) which is prevalent among children and teenagers. There is no single test that can diagnose ADHD. Symptoms must pose problems in at minimum two places (such as school, family, job, or leisure time) for at least six months to be diagnosed. Facing issues in paying attention by youngsters can lead to low academic performance. In addition to this, ADHD is sometimes linked to various mental illnesses and substance abuse issues, which can lead to further harm, especially in the contemporary generation. Unfortunately, ADHD is incurable. But early detection, together with an effective treatment and education plan, can help a child or adult with ADHD manage their symptoms. Therefore, this project attempts to classify ADHD using machine learning (ML) techniques in order to help provide valuable insights on establishing an automated diagnosis system. A comparative analysis of 4-way classification of ADHD using various machine learning algorithms has been done in WEKA toolkit (experimenter) while also experimenting with different subsets of features, including those created by applying genetic algorithm (GA), from the phenotypic characteristics of the ADHD-200 data set. The ML classifiers that have been used are Logistic, Support Vector Machine (SVM), Decision Tree (DT); implemented through J48 algorithm, Random Forest (RF), K-nearest neighbour (KNN); implemented through the instance-based learner (IBk) algorithm, and multi-layer perceptron (MLP). A total of 8 performance parameters were used for the evaluation of these classifiers: accuracy, precision, recall, F-measure, Kappa-statistic, root mean squared error (RMSE), Mathew's correlation coefficient (MCC), and area under the receiver operating characteristics (AUROC) curve.
{"title":"Classification of Attention Deficit Hyperactivity Disorder Using Machine Learning","authors":"Jivesh Singh, Gurpreet Kaur, Nitika Kapoor","doi":"10.1109/GCAT55367.2022.9971947","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971947","url":null,"abstract":"The global burden of mental ailments continues to rise, posing serious health risks as well as huge social, human rights, and economic ramifications in every country. One such disorder is the attention deficit hyperactivity disorder (ADHD) which is prevalent among children and teenagers. There is no single test that can diagnose ADHD. Symptoms must pose problems in at minimum two places (such as school, family, job, or leisure time) for at least six months to be diagnosed. Facing issues in paying attention by youngsters can lead to low academic performance. In addition to this, ADHD is sometimes linked to various mental illnesses and substance abuse issues, which can lead to further harm, especially in the contemporary generation. Unfortunately, ADHD is incurable. But early detection, together with an effective treatment and education plan, can help a child or adult with ADHD manage their symptoms. Therefore, this project attempts to classify ADHD using machine learning (ML) techniques in order to help provide valuable insights on establishing an automated diagnosis system. A comparative analysis of 4-way classification of ADHD using various machine learning algorithms has been done in WEKA toolkit (experimenter) while also experimenting with different subsets of features, including those created by applying genetic algorithm (GA), from the phenotypic characteristics of the ADHD-200 data set. The ML classifiers that have been used are Logistic, Support Vector Machine (SVM), Decision Tree (DT); implemented through J48 algorithm, Random Forest (RF), K-nearest neighbour (KNN); implemented through the instance-based learner (IBk) algorithm, and multi-layer perceptron (MLP). A total of 8 performance parameters were used for the evaluation of these classifiers: accuracy, precision, recall, F-measure, Kappa-statistic, root mean squared error (RMSE), Mathew's correlation coefficient (MCC), and area under the receiver operating characteristics (AUROC) curve.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128273098","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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