Pub Date : 2022-12-16DOI: 10.1109/SMART55829.2022.10047324
S. Pradeep, Yogesh Kumar Sharma, C. Verma, Neagu Bogdan Constantin, Z. Illés, M. Răboacă, Traian Candin Mihaltan
A severe hazard to human homes and forest ecosystems worldwide and many others circumstances are taking place, fires have a variety of detrimental effects. One result of such devastation is the greenhouse effect and changes to the climate. It's interesting to see that based on human activity and natural disasters like forest fires and power Fluctuation are increasing. Therefore, it's important to spot fires early on in order to reduce the damage that fires inflict. In this paper, the path are proposed for leveraging a WSN will initially starts to detect the fires. AI intelligence or deep learning techniques are taking part to give more accurate fire detection. Research on Artificial intelligence approaches like technical search and agents is extremely tempting for catastrophe look over like fire. Using AI approaches, a strategy for responding to fires is created. This is accomplished by combining WSN, CNN and AI agents. This work's outcome analysis is pretty effective.
{"title":"Utilizing WSN and Artificial Intelligence to Detect Fires","authors":"S. Pradeep, Yogesh Kumar Sharma, C. Verma, Neagu Bogdan Constantin, Z. Illés, M. Răboacă, Traian Candin Mihaltan","doi":"10.1109/SMART55829.2022.10047324","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047324","url":null,"abstract":"A severe hazard to human homes and forest ecosystems worldwide and many others circumstances are taking place, fires have a variety of detrimental effects. One result of such devastation is the greenhouse effect and changes to the climate. It's interesting to see that based on human activity and natural disasters like forest fires and power Fluctuation are increasing. Therefore, it's important to spot fires early on in order to reduce the damage that fires inflict. In this paper, the path are proposed for leveraging a WSN will initially starts to detect the fires. AI intelligence or deep learning techniques are taking part to give more accurate fire detection. Research on Artificial intelligence approaches like technical search and agents is extremely tempting for catastrophe look over like fire. Using AI approaches, a strategy for responding to fires is created. This is accomplished by combining WSN, CNN and AI agents. This work's outcome analysis is pretty effective.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"86 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":"122735847","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/SMART55829.2022.10046756
Shivani Joshi, B. S, Poonam Rawat, Deepali Deshpande, M. Chakravarthi, Devvret Verma
In order to prevent excessive energy usage and to identify water pollution, alternately, genuine process industry detection and picture categorization are now required. Scientists are looking for a limited and efficient IoT (Iot) device that would detect and assess the real-time state of industrial machinery since implementing automation in economic industries is often an expensive project. Additionally, the IoT technology may be used to classify images in order to find water contamination. This study has compared several picture binary classifiers and described the advantages and price of the IoT that is now accessible. On the basis of the opinions of returned questionnaires, a main numerical survey approach has been used to gather relevant data. After then, the “Normative” selecting method was used to analyse the main data and support a comparative evaluation. Internet of things iot (Sensor Networks) is a less advanced product that can be included into both small- and large-scale industrial businesses, according to research and analysis. For identifying contamination of water, classification IoT has been shown to be effective, and texture analysis is less expensive than spatial analysis.
{"title":"A Framework of Internet of Things (Iot) for the Manufacturing and Image Classification System","authors":"Shivani Joshi, B. S, Poonam Rawat, Deepali Deshpande, M. Chakravarthi, Devvret Verma","doi":"10.1109/SMART55829.2022.10046756","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046756","url":null,"abstract":"In order to prevent excessive energy usage and to identify water pollution, alternately, genuine process industry detection and picture categorization are now required. Scientists are looking for a limited and efficient IoT (Iot) device that would detect and assess the real-time state of industrial machinery since implementing automation in economic industries is often an expensive project. Additionally, the IoT technology may be used to classify images in order to find water contamination. This study has compared several picture binary classifiers and described the advantages and price of the IoT that is now accessible. On the basis of the opinions of returned questionnaires, a main numerical survey approach has been used to gather relevant data. After then, the “Normative” selecting method was used to analyse the main data and support a comparative evaluation. Internet of things iot (Sensor Networks) is a less advanced product that can be included into both small- and large-scale industrial businesses, according to research and analysis. For identifying contamination of water, classification IoT has been shown to be effective, and texture analysis is less expensive than spatial analysis.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"7 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":"121875817","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/SMART55829.2022.10046870
Ramakant Upadhyay, Harinder Kaur
The curve of stock is unexpected. The complexity and unpredictability of stock market predictions make them difficult to make. Predicting the stability of future market stocks is the main goal for persuading the audience. Numerous analysts have conducted their study on how the industry would evolve in the future. Unreliable information is a component of stock, making knowledge a vital source of power. Impact of the prediction's strength on enduring possibilities. Deep learning has incorporated itself into the image for the development and projection of instruction sets and information models as part of the current development of exchange forecasting technology. To forecast and alter things as needed, Machine Learning uses whole distinct components methods and algorithms. The main topic of the paper is the Application of LSTM and regression to forecast stock values.
{"title":"A ML Algorithm was used to Forecast the Gain or Loss of a Shareholder in the Financial Markets","authors":"Ramakant Upadhyay, Harinder Kaur","doi":"10.1109/SMART55829.2022.10046870","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046870","url":null,"abstract":"The curve of stock is unexpected. The complexity and unpredictability of stock market predictions make them difficult to make. Predicting the stability of future market stocks is the main goal for persuading the audience. Numerous analysts have conducted their study on how the industry would evolve in the future. Unreliable information is a component of stock, making knowledge a vital source of power. Impact of the prediction's strength on enduring possibilities. Deep learning has incorporated itself into the image for the development and projection of instruction sets and information models as part of the current development of exchange forecasting technology. To forecast and alter things as needed, Machine Learning uses whole distinct components methods and algorithms. The main topic of the paper is the Application of LSTM and regression to forecast stock values.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"52 2 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":"133337868","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/SMART55829.2022.10047018
Garima Sharma, Tanisha
In this article, we investigate how to build an intelligent network unit over a wireless network. To do so, we make use of a resilient routing strategy made available by the Protocol for power (RPL), the definition of which is currently being considered. Our architecture is based on a simple binary web service execution of the RE presentational State Transfer (REST) paradigm and an executing strategy in which every node makes a collection of components (such as atmospheric sensors) available to parties who are concerned with them. We present an evaluation of RPL by means of an experimental inquiry, with the focus being on how well it creates the routing structure to highlight how the efficiency of routing is influenced by the fundamental properties of RPL.
{"title":"Implementing a Smart Monitoring System with Wireless Sensor and Actuator Networks","authors":"Garima Sharma, Tanisha","doi":"10.1109/SMART55829.2022.10047018","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047018","url":null,"abstract":"In this article, we investigate how to build an intelligent network unit over a wireless network. To do so, we make use of a resilient routing strategy made available by the Protocol for power (RPL), the definition of which is currently being considered. Our architecture is based on a simple binary web service execution of the RE presentational State Transfer (REST) paradigm and an executing strategy in which every node makes a collection of components (such as atmospheric sensors) available to parties who are concerned with them. We present an evaluation of RPL by means of an experimental inquiry, with the focus being on how well it creates the routing structure to highlight how the efficiency of routing is influenced by the fundamental properties of RPL.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"26 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":"133497228","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/SMART55829.2022.10046896
Ashwini Kshirsagar, S. Pranavan, M. Nomani, V. Srivastav, C. Ramprasad, Surendra Kumar Shukla
Urban dwellers' quality of life may be improved by adopting unified, extensible, yet secure e - services thanks to the ongoing urban growth. Government entities are aware of how useful blockchains can be in addressing community issues. Bitcoin, which was primarily associated with the digital money bitcoins, provides a novel viewpoint as to how cities might be structured as well as a more open economical system for managing resources. This paper examines the potential benefits of ledger tech businesses for the growth of smart communities and suggests a Smart City ecological architecture based on intelligent contract involving firms, residents, or government agencies. It also provides an outline of the possible application areas for this technology. The findings might serve as a springboard for the creation of regional efforts to use the cryptocurrency as a framework for transactions and communications with in government service.
{"title":"Ecosystem Implementations in Smart City Through Block Chain Technology","authors":"Ashwini Kshirsagar, S. Pranavan, M. Nomani, V. Srivastav, C. Ramprasad, Surendra Kumar Shukla","doi":"10.1109/SMART55829.2022.10046896","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046896","url":null,"abstract":"Urban dwellers' quality of life may be improved by adopting unified, extensible, yet secure e - services thanks to the ongoing urban growth. Government entities are aware of how useful blockchains can be in addressing community issues. Bitcoin, which was primarily associated with the digital money bitcoins, provides a novel viewpoint as to how cities might be structured as well as a more open economical system for managing resources. This paper examines the potential benefits of ledger tech businesses for the growth of smart communities and suggests a Smart City ecological architecture based on intelligent contract involving firms, residents, or government agencies. It also provides an outline of the possible application areas for this technology. The findings might serve as a springboard for the creation of regional efforts to use the cryptocurrency as a framework for transactions and communications with in government service.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"86 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131770569","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.
{"title":"Design of Coaxial Feed Microstrip Patch Antenna to Reduce Return Loss and Comparing with Square Shaped Antenna","authors":"G. A. Kumar, G. Uganya","doi":"10.1109/SMART55829.2022.10047772","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047772","url":null,"abstract":"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.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"5 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":"132263680","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/SMART55829.2022.10047696
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。
{"title":"Intrusion Detection Using Enhanced Transductive Support Vector Machine","authors":"V. Priyalakshmi, R. Devi","doi":"10.1109/SMART55829.2022.10047696","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047696","url":null,"abstract":"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.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"57 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":"134200708","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/SMART55829.2022.10047387
P. Pant, A. Rajawat, S. B. Goyal, Deepmala Singh, Neagu Bogdan Constantin, M. Răboacă, C. Verma
Machine learning, with its huge untapped potential, is being researched all over the world to develop truly intelligent systems. Its applications are not enclosed in just one domain but in almost everything, from prediction models, recommender systems, and anomaly detection to automation and teaching a computer how to fly a helicopter. In this research, Multivariate Linear regression of supervised machine learning is studied to predict the efficiency of Industry 5.0, however, the efficiency of the model would be dependent on many factors and components such as security protocols and models, Industrial IoT - performance, connectivity, reachability, availability and many more. These factors and components would be categorized as the features of the algorithm which would be assigned weight ‘w’ and bias ‘b’. To improve the efficiency of the model, these components could be changed and updated in order to enhance the overall model. Previous research papers discussed the integration of “hot” technologies like 5G, Blockchain, AI, and IIoT in the industry 5.0 model, but this research is presented as their future work as it proposes to determine the efficiency of the model based on the features provided so that ultimate and optimal model could be determined. Later it proposes security and IIoT models that could improve the overall Industry 5.0. Quorum blockchain is proposed by the research in order to implement the ultimate security in the Industry 5.0.
机器学习具有巨大的未开发潜力，世界各地都在研究它，以开发真正的智能系统。它的应用并不局限于一个领域，而是几乎在所有领域，从预测模型、推荐系统、异常检测到自动化和教计算机如何驾驶直升机。在本研究中，研究了监督机器学习的多元线性回归来预测工业5.0的效率，然而，模型的效率将取决于许多因素和组件，如安全协议和模型，工业物联网性能，连接性，可达性，可用性等等。这些因素和组成部分将被归类为算法的特征，这些特征将被分配权重' w '和偏差' b '。为了提高模型的效率，可以对这些组件进行更改和更新，以增强整体模型。之前的研究论文讨论了5G、区块链、AI、IIoT等“热门”技术在工业5.0模型中的集成，但本研究是他们未来的工作，提出根据所提供的特征来确定模型的效率，从而确定最终和最优的模型。随后，它提出了可以改善整体工业5.0的安全和工业物联网模型。为了实现工业5.0的终极安全，本研究提出了仲裁区块链。
{"title":"Using Machine Learning for Industry 5.0 Efficiency Prediction Based on Security and Proposing Models to Enhance Efficiency","authors":"P. Pant, A. Rajawat, S. B. Goyal, Deepmala Singh, Neagu Bogdan Constantin, M. Răboacă, C. Verma","doi":"10.1109/SMART55829.2022.10047387","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047387","url":null,"abstract":"Machine learning, with its huge untapped potential, is being researched all over the world to develop truly intelligent systems. Its applications are not enclosed in just one domain but in almost everything, from prediction models, recommender systems, and anomaly detection to automation and teaching a computer how to fly a helicopter. In this research, Multivariate Linear regression of supervised machine learning is studied to predict the efficiency of Industry 5.0, however, the efficiency of the model would be dependent on many factors and components such as security protocols and models, Industrial IoT - performance, connectivity, reachability, availability and many more. These factors and components would be categorized as the features of the algorithm which would be assigned weight ‘w’ and bias ‘b’. To improve the efficiency of the model, these components could be changed and updated in order to enhance the overall model. Previous research papers discussed the integration of “hot” technologies like 5G, Blockchain, AI, and IIoT in the industry 5.0 model, but this research is presented as their future work as it proposes to determine the efficiency of the model based on the features provided so that ultimate and optimal model could be determined. Later it proposes security and IIoT models that could improve the overall Industry 5.0. Quorum blockchain is proposed by the research in order to implement the ultimate security in the Industry 5.0.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"214 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":"131605548","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/SMART55829.2022.10047123
Mizan Ali Khan, H. Kaur
Predicting student behavior and achievement in the present educational system is becoming more challenging. If we are able to forecast student performance in the past, it will be easier for both students and their teachers to monitor their progress and activities. Nowadays, the continuous assessment approach has been implemented by several colleges all around the world. Such technologies are helpful to students in raising their grades and performance, as well as to instructors in assessing the pupils and concentrating on those who exhibit poor performance. This assessment system's primary purpose is to assist all normal students and teachers. Artificial Neural Networks (ANN) have recently seen widespread and successful implementations in a wide range of data mining methods and applications, and are frequently far superior to other classifiers, whether they be machine learning representations and others like training algorithm, stochastic gradient descent, or minibatch. In light of educational data mining, the purpose of this article is to determine if artificial neural networks (ANN) are an effective predictive classifier to forecast students' performance using a dataset from a learning system. On this dataset of LMS, we will evaluate the performance of neural networks to that of several other classifiers in order to assess their applicability. Support Vector Machine (SVM) is one of these classifiers.
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