Pub Date : 2022-12-16DOI: 10.1109/SMART55829.2022.10047800
Joel Alanya ‒ Beltran, Jesús Padilla-Caballero, F. A. Ochoa Tataje, Claudia Poma ‒ Garcia, Adolfo Perez ‒ Mendoza, Trishu Sharma
Occupational category or education professionals at all degrees have always been drawn to emerging innovations in order to maximize their potential for working hard and enriching the experiences of teaching and learning of both instructors and students. The increased usage of Big Data, Business Intelligence, and Intelligent Systems are a few of the breakthroughs. The usage of Block chain technology is another emerging field that is receiving attention from the education industry. The contribution to the Ir4.0 phenomena is the Block chain. It is a new technology that operates according to the autonomy and dispersion principles. The book “Block Chain Uses for School,” edited in Ramesh Vijay Yadav, Hadi Heidari, and GulsunKurubacak, was thoroughly reviewed in the article. It is among the few books that highlights the use of bitcoin technology in the classroom. The writers of the corresponding pages include a variety of potential Public Block chain management for schooling application. Additionally, the eBook focuses on managing institutions databases employing block chain, which increases the validity and safety of school data related to diplomas and grading sheet by enabling failsafe verification for all shareholders. In a couple of the pages, it is discussed how open and remote institutions of learning may handle large data using block chain. The book is essential reading for anybody involved in creating educational policies, putting them into practise, or studying education in general since it offers fresh perspectives on the numerous facets of this relatively recent field of technology application in education. This would assist students in changing how they see educational procedures.
{"title":"Implementation and Execution of Block Chain Technology in the Field of Education","authors":"Joel Alanya ‒ Beltran, Jesús Padilla-Caballero, F. A. Ochoa Tataje, Claudia Poma ‒ Garcia, Adolfo Perez ‒ Mendoza, Trishu Sharma","doi":"10.1109/SMART55829.2022.10047800","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047800","url":null,"abstract":"Occupational category or education professionals at all degrees have always been drawn to emerging innovations in order to maximize their potential for working hard and enriching the experiences of teaching and learning of both instructors and students. The increased usage of Big Data, Business Intelligence, and Intelligent Systems are a few of the breakthroughs. The usage of Block chain technology is another emerging field that is receiving attention from the education industry. The contribution to the Ir4.0 phenomena is the Block chain. It is a new technology that operates according to the autonomy and dispersion principles. The book “Block Chain Uses for School,” edited in Ramesh Vijay Yadav, Hadi Heidari, and GulsunKurubacak, was thoroughly reviewed in the article. It is among the few books that highlights the use of bitcoin technology in the classroom. The writers of the corresponding pages include a variety of potential Public Block chain management for schooling application. Additionally, the eBook focuses on managing institutions databases employing block chain, which increases the validity and safety of school data related to diplomas and grading sheet by enabling failsafe verification for all shareholders. In a couple of the pages, it is discussed how open and remote institutions of learning may handle large data using block chain. The book is essential reading for anybody involved in creating educational policies, putting them into practise, or studying education in general since it offers fresh perspectives on the numerous facets of this relatively recent field of technology application in education. This would assist students in changing how they see educational procedures.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125260790","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047572
R. Jain
Water is the most widely consumed natural resource on the earth. Due to continuous use and unmindful wastage, the water table is declining. To protect this information of ground water potential is needed. Remote Sensing with Geographic Information System and Multi criteria decision analysis techniques is used. Analytical Hierarchy Process comes under Multi Criteria Decision Analysis and it is executed for defining weights for different criteria
{"title":"Groundwater Delineation Using RS and GIS for Gurgaon Region","authors":"R. Jain","doi":"10.1109/SMART55829.2022.10047572","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047572","url":null,"abstract":"Water is the most widely consumed natural resource on the earth. Due to continuous use and unmindful wastage, the water table is declining. To protect this information of ground water potential is needed. Remote Sensing with Geographic Information System and Multi criteria decision analysis techniques is used. Analytical Hierarchy Process comes under Multi Criteria Decision Analysis and it is executed for defining weights for different criteria","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779373","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047372
Hritik Gupta, A. Chaudhary, Anil Kumar
Java is still regarded as one of the most powerful programming languages available, because of its security and platform independence. It's hard to manage logs manually so to simplify and to make logging easy Apache released Apache log4j framework to manage logs generated by applications easily. This is imbued within the code so no extra hard work is required to access or deploy it. This paper is all about logg4j vulnerabilities visible in the log4j framework.
{"title":"Identification and Analysis of Log4j Vulnerability","authors":"Hritik Gupta, A. Chaudhary, Anil Kumar","doi":"10.1109/SMART55829.2022.10047372","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047372","url":null,"abstract":"Java is still regarded as one of the most powerful programming languages available, because of its security and platform independence. It's hard to manage logs manually so to simplify and to make logging easy Apache released Apache log4j framework to manage logs generated by applications easily. This is imbued within the code so no extra hard work is required to access or deploy it. This paper is all about logg4j vulnerabilities visible in the log4j framework.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129047853","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047151
A. Kumar, K. Sutariya
Denoising an image has become an extremely important step in medical imaging, and it is performed throughout the entire diagnostic process. In medical imaging, it is imperative that a balance be maintained between the elimination of distracting noise and the maintenance of diagnostically relevant information. Imaging modalities have many objectives, one of the most important of which is to supply the doctor with the most reliable information possible so that they can make an precise diagnosis. The utilization of multiresolution noise filters in a wide range of medical imaging applications is garnering an increasing amount of attention. This study discusses some of the possible uses of new wavelet denoising algorithms for medical magnetic resonance images and reviews some of the techniques that have been used recently. These techniques were used to investigate various areas of the human body. The goal of this project is to demonstrate and evaluate various approaches of noise suppression that are based on both image processing and clinical experience. Rician noise is a phenomenon that is frequently observed in magnetic resonance imaging (MRI). In the field of medical image processing, edge-preserving denoising is becoming an increasingly important technique. In this paper, a wavelet-based multi scale products thresholding system is presented for the purpose of eliminating noise in magnetic resonance pictures. A dyadic wavelet transform that works similarly to an edge detector is used. As a consequence of this, significant features in images will continue to evolve with high magnitude throughout wavelet scales, whereas noise will quickly fade away. The wavelet sub bands that are next to one another are multiplied in order to improve edge structures while simultaneously reducing noise in order to take advantage of wavelet inter scale dependencies. When using the multi scale products, it is possible to differentiate edges from noise in an efficient manner. After that, an adaptive threshold is computed and applied to the products rather than the wavelet coefficients so that relevant features can be identified. Experiments have demonstrated that adaptive multi scale products thresholding is superior to conventional wavelet-thresholding denoising approaches in terms of its ability to reduce noise and retain edges. The fact that the wavelet transform can recreate an image without any noticeable loss of quality is the primary benefit of using this technique.
{"title":"Adaptive Multi Scale Products Threshold-Based MRI Denoising","authors":"A. Kumar, K. Sutariya","doi":"10.1109/SMART55829.2022.10047151","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047151","url":null,"abstract":"Denoising an image has become an extremely important step in medical imaging, and it is performed throughout the entire diagnostic process. In medical imaging, it is imperative that a balance be maintained between the elimination of distracting noise and the maintenance of diagnostically relevant information. Imaging modalities have many objectives, one of the most important of which is to supply the doctor with the most reliable information possible so that they can make an precise diagnosis. The utilization of multiresolution noise filters in a wide range of medical imaging applications is garnering an increasing amount of attention. This study discusses some of the possible uses of new wavelet denoising algorithms for medical magnetic resonance images and reviews some of the techniques that have been used recently. These techniques were used to investigate various areas of the human body. The goal of this project is to demonstrate and evaluate various approaches of noise suppression that are based on both image processing and clinical experience. Rician noise is a phenomenon that is frequently observed in magnetic resonance imaging (MRI). In the field of medical image processing, edge-preserving denoising is becoming an increasingly important technique. In this paper, a wavelet-based multi scale products thresholding system is presented for the purpose of eliminating noise in magnetic resonance pictures. A dyadic wavelet transform that works similarly to an edge detector is used. As a consequence of this, significant features in images will continue to evolve with high magnitude throughout wavelet scales, whereas noise will quickly fade away. The wavelet sub bands that are next to one another are multiplied in order to improve edge structures while simultaneously reducing noise in order to take advantage of wavelet inter scale dependencies. When using the multi scale products, it is possible to differentiate edges from noise in an efficient manner. After that, an adaptive threshold is computed and applied to the products rather than the wavelet coefficients so that relevant features can be identified. Experiments have demonstrated that adaptive multi scale products thresholding is superior to conventional wavelet-thresholding denoising approaches in terms of its ability to reduce noise and retain edges. The fact that the wavelet transform can recreate an image without any noticeable loss of quality is the primary benefit of using this technique.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132397885","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047391
Yashi Bajpai, Madhavi Srivastva, T. Singh, Vineet Kumar Chauhan, Diwakar Upadhyay, Abhishek Dixit
One of the industries that are most crucial to humanity is agriculture. Agriculture mechanization is the major issue facing all countries today. As the world's population is expanding at an incredibly fast rate, there is an increasing demand for food. To fulfill the expanding demand, farmers will need to apply chemical pesticides more often than they already do. The soil is harmed by this. The land continues to be unproductive and barren as a result of this having a substantial influence on agricultural activities. Several mechanization strategies, including deep learning, machine learning, and artificial intelligence, are covered in this article. It is crucial to use new technologies at various stages of the agro-based supply chain due to several long-term challenges for the agricultural industry and various factors, such as population growth, global warming, technological advancement, and the condition of environmental assets (water, etc.). Examples include automated farm equipment processes, the use of sensing devices and satellite data for distant locations, artificial intelligence, and machine learning for forecasting weather patterns. Crop diseases, inadequate storage management, chemical usage, weed control, insufficient irrigation, and poor water management are just a few problems the agricultural sector is facing. Using the range of strategies covered, each of these problems might be handled. It has been demonstrated that automating farming procedures increases soil productivity and improves soil fertility. To get a quick overview of how automation is currently being used in agriculture, this paper examines the work of numerous researchers. In the current study, we highlight the key uses of AI and Ml techniques in farming and highlight the undeniably rising trend in the implementation of these techniques to advance the agriculture sector.
{"title":"Analysis of Agricultural Toolset based on Artificial Intelligence","authors":"Yashi Bajpai, Madhavi Srivastva, T. Singh, Vineet Kumar Chauhan, Diwakar Upadhyay, Abhishek Dixit","doi":"10.1109/SMART55829.2022.10047391","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047391","url":null,"abstract":"One of the industries that are most crucial to humanity is agriculture. Agriculture mechanization is the major issue facing all countries today. As the world's population is expanding at an incredibly fast rate, there is an increasing demand for food. To fulfill the expanding demand, farmers will need to apply chemical pesticides more often than they already do. The soil is harmed by this. The land continues to be unproductive and barren as a result of this having a substantial influence on agricultural activities. Several mechanization strategies, including deep learning, machine learning, and artificial intelligence, are covered in this article. It is crucial to use new technologies at various stages of the agro-based supply chain due to several long-term challenges for the agricultural industry and various factors, such as population growth, global warming, technological advancement, and the condition of environmental assets (water, etc.). Examples include automated farm equipment processes, the use of sensing devices and satellite data for distant locations, artificial intelligence, and machine learning for forecasting weather patterns. Crop diseases, inadequate storage management, chemical usage, weed control, insufficient irrigation, and poor water management are just a few problems the agricultural sector is facing. Using the range of strategies covered, each of these problems might be handled. It has been demonstrated that automating farming procedures increases soil productivity and improves soil fertility. To get a quick overview of how automation is currently being used in agriculture, this paper examines the work of numerous researchers. In the current study, we highlight the key uses of AI and Ml techniques in farming and highlight the undeniably rising trend in the implementation of these techniques to advance the agriculture sector.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134009999","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10046821
Anil Kumar, R. Shukla, R. Shukla, Shelendra Pal
Wireless topologies can change rapidly and unexpectedly. Hybrid networks are connected by gateways, which act as entry points for all traffic. Some gateways may be subject to high traffic loads and therefore may be overloaded. To compare the performance of the proposed work, various parameters including end-to-end latency, power consumption, and throughput are considered. Load Balancing Hybrid Simulator (LBHS) is an algorithm used in this paper to distribute traffic load among multiple gateways to solve load balancing problems in MANET. It combines a Hybridge Annealing Simulation (HAS) network technology with Decentralized Random Search (DRS). A network that supports QoS in MANET. The Dominant Minimal Set (DMS) problem is solved using AS. The DSS heuristic algorithm provides an algorithm with a simple structure and a high degree of exploration. In addition to faster convergence than other algorithms, it also implements an axiomatic algorithm to ensure the diversity of agents and avoid falling into local optimum. The test results show that the proposed Hybridge Annealing Simulated Decentralized Random Search (HAS-DRS) procedure performs better than the dominated connection set (DCS).
{"title":"Enhancement of Quality of Service in Mobile Ad-Hoc Networks using Hybrid Load Balanced Simulator: Annealing Simulated","authors":"Anil Kumar, R. Shukla, R. Shukla, Shelendra Pal","doi":"10.1109/SMART55829.2022.10046821","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046821","url":null,"abstract":"Wireless topologies can change rapidly and unexpectedly. Hybrid networks are connected by gateways, which act as entry points for all traffic. Some gateways may be subject to high traffic loads and therefore may be overloaded. To compare the performance of the proposed work, various parameters including end-to-end latency, power consumption, and throughput are considered. Load Balancing Hybrid Simulator (LBHS) is an algorithm used in this paper to distribute traffic load among multiple gateways to solve load balancing problems in MANET. It combines a Hybridge Annealing Simulation (HAS) network technology with Decentralized Random Search (DRS). A network that supports QoS in MANET. The Dominant Minimal Set (DMS) problem is solved using AS. The DSS heuristic algorithm provides an algorithm with a simple structure and a high degree of exploration. In addition to faster convergence than other algorithms, it also implements an axiomatic algorithm to ensure the diversity of agents and avoid falling into local optimum. The test results show that the proposed Hybridge Annealing Simulated Decentralized Random Search (HAS-DRS) procedure performs better than the dominated connection set (DCS).","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133677210","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047622
D. Gritto, P. Muthulakshmi
Cloud computing is a service model that has evolved in its stature beyond its traditional bounds of infrastructure, platform and software as a service. As the surge in resource demand may hit the cloud service provider at any time, a ceaseless monitoring system is vital. The allocation of an appropriate virtual machine for the cloudlet i.e., the user workload and maintaining the work load equilibrium among the resources is the most challenging operation in the cloud environment. The proper utilization of the cloud resources can be ensured by selecting the right cloudlet scheduling and load balancing algorithm(s). The cloudlet scheduling algorithm selection is based on the combination of two or more Quality of Service (QoS) and performance metrics like makespan, throughput, cost, power consumption, virtual machine or resource utilization and load balancing etc. The load balancer module takes the responsibility of dispersing the cloudlets evenly among the virtual machines by considering various features like CPU utilization, number of processing elements, bandwidth, memory and the load limit of the virtual machines. In this paper, an effort has been made to comprehend the most persisting cloudlet scheduling and load balancing algorithms that have been proposed by the researchers. Compiling the load balancing technologies that are integrated with the contemporary cloud platforms such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) has also been prioritized. This study suggests a Priority Based Cloudlet Scheduling Algorithm (PBCSA) that schedules the cloudlet according to the user priority. The Min-Min scheduler is used to schedule the high priority cloudlets and the Max-Min scheduler is used to schedule the low priority cloudlets. The experimental findings reveals that, in the majority of scenarios, the proposed algorithm outperforms the Min-Min and Max-Min scheduling in terms of makespan and virtual machine utilization ratio.
云计算是一种服务模式，其地位已经超越了基础设施、平台和软件即服务的传统界限。由于资源需求的激增可能随时冲击云服务提供商，因此一个不间断的监控系统至关重要。为cloudlet分配适当的虚拟机(即用户工作负载和维护资源之间的工作负载平衡)是云环境中最具挑战性的操作。通过选择合适的云调度和负载均衡算法，可以保证云资源的合理利用。cloudlet调度算法的选择是基于两个或多个服务质量(QoS)和性能指标的组合，如makespan、吞吐量、成本、功耗、虚拟机或资源利用率和负载平衡等。负载平衡器模块通过考虑各种特性，如CPU利用率、处理元素的数量、带宽、内存和虚拟机的负载限制，负责在虚拟机中均匀地分散cloudlet。本文对研究人员提出的最持久的云调度和负载平衡算法进行了理解。编译与Amazon Web Services (AWS)、Microsoft Azure和谷歌cloud Platform (GCP)等当代云平台集成的负载平衡技术也已被优先考虑。本研究提出一种基于优先级的云调度算法(PBCSA)，根据用户优先级对云调度进行调度。Min-Min调度器用于调度高优先级的cloudlets, Max-Min调度器用于调度低优先级的cloudlets。实验结果表明，在大多数场景下，该算法在makespan和虚拟机利用率方面优于Min-Min和Max-Min调度。
{"title":"Scheduling Cloudlets in a Cloud Computing Environment: A Priority-based Cloudlet Scheduling Algorithm (PBCSA)","authors":"D. Gritto, P. Muthulakshmi","doi":"10.1109/SMART55829.2022.10047622","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047622","url":null,"abstract":"Cloud computing is a service model that has evolved in its stature beyond its traditional bounds of infrastructure, platform and software as a service. As the surge in resource demand may hit the cloud service provider at any time, a ceaseless monitoring system is vital. The allocation of an appropriate virtual machine for the cloudlet i.e., the user workload and maintaining the work load equilibrium among the resources is the most challenging operation in the cloud environment. The proper utilization of the cloud resources can be ensured by selecting the right cloudlet scheduling and load balancing algorithm(s). The cloudlet scheduling algorithm selection is based on the combination of two or more Quality of Service (QoS) and performance metrics like makespan, throughput, cost, power consumption, virtual machine or resource utilization and load balancing etc. The load balancer module takes the responsibility of dispersing the cloudlets evenly among the virtual machines by considering various features like CPU utilization, number of processing elements, bandwidth, memory and the load limit of the virtual machines. In this paper, an effort has been made to comprehend the most persisting cloudlet scheduling and load balancing algorithms that have been proposed by the researchers. Compiling the load balancing technologies that are integrated with the contemporary cloud platforms such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) has also been prioritized. This study suggests a Priority Based Cloudlet Scheduling Algorithm (PBCSA) that schedules the cloudlet according to the user priority. The Min-Min scheduler is used to schedule the high priority cloudlets and the Max-Min scheduler is used to schedule the low priority cloudlets. The experimental findings reveals that, in the majority of scenarios, the proposed algorithm outperforms the Min-Min and Max-Min scheduling in terms of makespan and virtual machine utilization ratio.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130696354","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047402
A. K. Moharana, Daxa Vekariya
Dermatological issues are one of the most preventable diseases in the world. Although it is widespread, studying it is challenging because of the many layers of complexity introduced by the presence of colour, concealment, and hair. Diagnosing skin problems early is essential for effective therapy. The method for identifying and treating skin injury is based on the specialist's level of competence and experience. There needs to be pinpoint accuracy in the analysis. Success rates for clinical diagnostic and clinical therapeutic frameworks are improving with time as a result of cutting-edge developments in medicine and data science. Skin disease diagnosis has benefited from the application of AI calculations and the utilisation of the large quantity of information available in hospitals and clinics. For this study, we collated a large number of previous studies that analysed skin illnesses via the lens of AI-based classification strategies. In their previous studies, the specialists employed numerous frameworks, instruments, and calculations. A small number of frameworks have been developed that are capable of correctly identifying skin diseases with varying degrees of suggestive precision. Multiple models have used image processing and component extraction methods to
{"title":"Detection of Skin Diseases via Deep Learning using SVM Method","authors":"A. K. Moharana, Daxa Vekariya","doi":"10.1109/SMART55829.2022.10047402","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047402","url":null,"abstract":"Dermatological issues are one of the most preventable diseases in the world. Although it is widespread, studying it is challenging because of the many layers of complexity introduced by the presence of colour, concealment, and hair. Diagnosing skin problems early is essential for effective therapy. The method for identifying and treating skin injury is based on the specialist's level of competence and experience. There needs to be pinpoint accuracy in the analysis. Success rates for clinical diagnostic and clinical therapeutic frameworks are improving with time as a result of cutting-edge developments in medicine and data science. Skin disease diagnosis has benefited from the application of AI calculations and the utilisation of the large quantity of information available in hospitals and clinics. For this study, we collated a large number of previous studies that analysed skin illnesses via the lens of AI-based classification strategies. In their previous studies, the specialists employed numerous frameworks, instruments, and calculations. A small number of frameworks have been developed that are capable of correctly identifying skin diseases with varying degrees of suggestive precision. Multiple models have used image processing and component extraction methods to","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131200433","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047772
G. A. Kumar, G. Uganya
The main aim of the work involves designing a novel coaxial feed microstrip patch antenna to reduce return loss by comparing with the square shaped antenna. The desired antenna is made using a rectangular structure that was built on a Rogers RO4350 material with 3.6 dielectric constant, with 3.2 mm substrate height. The performance of the antenna is designed and analyzed servicing Ansoft HFSS 13.0 software. The estimated total sample size is considered to be 40 using 80% of pretest power. Group 1 is considered as coaxial feed MPA and group 2 is considered as square shaped antenna. The co-axial microstrip patch antenna is having return loss of −12.32 dB at 5.4GHz frequency, return loss of the square shaped antenna is −4.35 dB. It has been seen that the significance gap between the two groups is P<0.05. The return loss of novel coaxial feed microstrip patch antenna is significantly less when compared to square shaped antenna.
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Pub Date : 2022-12-16DOI: 10.1109/SMART55829.2022.10047696
V. Priyalakshmi, R. Devi
The world is getting more interconnected and reliant on the Internet and the services it provides today. The protection of networks and apps from unauthorized attacks is one of the biggest difficulties in internet communication. Numerous solutions have been put out to deal with security concerns, yet the vast majority of these solutions consistently fall short of rapidly and effectively detecting security threats. In order to detect new attacks with high accuracy, a method for intrusion detection employing machine learning techniques is proposed in this article. Here, the Enhanced Transductive Support Vector Machine (ETSVM) method is used to classify the data in order to more accurately detect the different types of intrusion attacks. The more pertinent and ideal features are chosen using the Improved Glowworm Swarm Optimization (IGSO) technique. This method performs better at detecting intrusions on the KDD CUP99 and CSE-CIC-IDS2018 datasets. Precision, recall, and accuracy are used to assess the proposed model's performance in identifying the four types of cyber attacks-DoS, U2R, R2L, and Probe. In order to validate the proposed methodology, comparative findings are presented.
当今世界越来越相互联系，越来越依赖互联网及其提供的服务。保护网络和应用程序免受未经授权的攻击是互联网通信中最大的困难之一。已经提出了许多解决方案来处理安全问题，但是这些解决方案中的绝大多数始终无法快速有效地检测安全威胁。为了高精度地检测新的攻击，本文提出了一种利用机器学习技术进行入侵检测的方法。本文采用增强的转换支持向量机(Enhanced Transductive Support Vector Machine, ETSVM)方法对数据进行分类，以便更准确地检测出不同类型的入侵攻击。利用改进的萤火虫群优化(IGSO)技术选择更有针对性和更理想的特征。该方法对KDD CUP99和CSE-CIC-IDS2018数据集的入侵检测效果较好。精确度、召回率和准确性被用来评估所提出的模型在识别四种类型的网络攻击(dos、U2R、R2L和Probe)方面的性能。为了验证所提出的方法，提出了比较结果。
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